# GTM AI — Full Content Dump > ZoomInfo's GTM AI platform documentation, guides, blog, marketplace, and release notes. > Source: https://gtm.ai > Index: https://gtm.ai/llms.txt ================================================================================ # Documentation ================================================================================ # Getting Started > GTM.AI is a context layer built on ZoomInfo's B2B intelligence that makes AI genuinely useful for go-to-market work. Start here. **Date:** 2026-03-24 **Source:** https://gtm.ai/docs --- General-purpose AI fails at GTM work because it has no grounding in live B2B reality. It doesn't know what companies are actively hiring, which accounts are showing intent signals, who just changed jobs, or how contacts at the same account are actually related. It hallucinates org charts and produces generic outreach. GTM.AI is the context layer that fixes this. It connects AI tools to ZoomInfo's B2B intelligence: 100M+ companies, 600M+ contacts, a living context graph of signals and relationships, and, for teams already using ZoomInfo, first-party context from your own CRM and conversation history. The result is AI that can do real GTM work. These docs cover the integration layer that brings it into your workflow: the ZoomInfo MCP server, the tools available through it, how credits are consumed, and how to connect and configure any MCP-compatible client. ## Explore the docs - [ZoomInfo MCP](/docs/mcp): Connect Claude, ChatGPT, or any MCP-compatible tool to ZoomInfo's intelligence. - [Tools Reference](/docs/mcp/tools): All twelve MCP tools: what they do, what they return, and what they cost. - [API Reference](https://docs.zoominfo.com/docs): Build custom integrations with the ZoomInfo API. - [Marketplace](/marketplace): Pre-built audiences, skills, and data sets to start from. ================================================================================ # ZoomInfo MCP > Connect any MCP-compatible AI tool to ZoomInfo's B2B intelligence: 100M+ companies, 600M+ contacts, signals, and first-party context. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp --- ## What is ZoomInfo MCP? The ZoomInfo Model Context Protocol (MCP) server connects any MCP-compatible AI tool directly to ZoomInfo's B2B intelligence platform. Ask an AI assistant to research an account and it draws from the same verified data your revenue team already trusts, not a web search. Without ZoomInfo connected, an AI tool researching an account returns a summary anyone could find on Google. With ZoomInfo connected, the same question returns verified decision-makers, current tech stack, hiring signals, and competitive intelligence. Conversation Intelligence users also get what was discussed on your team's last call with that account. ## Who is this for? **Revenue professionals** (AEs, SDRs, Account Managers, RevOps, Marketing Ops, and Product Marketing) using AI tools for account research, prospecting, competitive intelligence, outreach, and data enrichment. **Enterprise engineering teams** building AI-powered GTM applications who need grounded, verified B2B data to inform AI reasoning, not web-scraped summaries. ## Requirements - A ZoomInfo subscription (Sales, Copilot, Studio, or ZI Lite) - Bulk data credits enabled on your account (MCP does not work with recurring monthly credits) - An MCP-compatible AI tool, or your own MCP client. See [Clients](/docs/mcp/clients) - Each user authenticates with their own ZoomInfo credentials for the best experience (user-level context and personalized tooling) - App-level authentication is available for teams that require it > **New tools are added automatically** > > When ZoomInfo publishes new MCP tools, they become available at the start of your next session. No configuration required. ## What's available - [Getting Started](/docs/mcp/getting-started): Requirements and initial setup. - [Clients](/docs/mcp/clients): Setup guides for Claude, ChatGPT, Microsoft Copilot, Perplexity, and custom integrations. - [Tools](/docs/mcp/tools): Every tool: what it does and what it costs. - [Credits & Billing](/docs/mcp/credits): How data and AI credits are consumed. - [Security & Data](/docs/mcp/security): Authentication, entitlements, and data handling. - [FAQ & Troubleshooting](/docs/mcp/faq): Common questions and error resolution. ================================================================================ # Clients > Setup guides for connecting ZoomInfo MCP to Claude, ChatGPT, Microsoft Copilot, Perplexity, and custom-built AI tools. **Date:** 2026-05-06 **Source:** https://gtm.ai/docs/mcp/clients --- ## Supported Clients ZoomInfo MCP works with any AI tool that supports the Model Context Protocol. The architecture is client-agnostic: the same server URL and authentication flow applies regardless of which client you use. - [Claude](/docs/mcp/clients/claude): Listed in the Claude connector marketplace. Easiest setup. - [ChatGPT](/docs/mcp/clients/chatgpt): Listed in the ChatGPT app marketplace. Works on all tiers, including free plans. - [Microsoft Copilot](/docs/mcp/clients/microsoft-copilot): Listed in the Copilot Studio marketplace. Add as a tool to any agent. - [Perplexity](/docs/mcp/clients/perplexity): Native connector for paid Perplexity plans with Computer access. - [Custom Integration](/docs/mcp/clients/custom): For engineering teams building their own MCP-compatible AI tools. ## MCP Server URL All clients use the same endpoint: ``` https://mcp.zoominfo.com/mcp ``` ## Other Clients ### Claude Code (CLI) Anthropic's CLI for agentic coding supports MCP via the `claude mcp add` command. There is no connector marketplace. See the [Claude Code setup guide](/guides/how-to-connect-to-zoominfo-mcp-in-claude-code) for the full walkthrough, including DevPortal app creation and the OAuth callback. ### Codex OpenAI's coding agent supports MCP through a Custom MCP entry in Settings. Setup is short. Paste `https://mcp.zoominfo.com/mcp` as a Streamable HTTP server and complete the ZoomInfo login. Full walkthrough: [Codex setup guide](/guides/how-to-connect-to-zoominfo-mcp-in-codex). ### Gemini Gemini Enterprise supports custom MCP servers. Your organization must be on Google's allowlist first (contact your Google account manager). Once allowlisted, use `https://mcp.zoominfo.com/mcp` as the server URL in your Gemini MCP configuration. ================================================================================ # Tools > All ZoomInfo MCP tools: what they do, when they're used, and what credits they consume. **Date:** 2026-04-21 **Source:** https://gtm.ai/docs/mcp/tools --- ## How Tools Work When you send a prompt, the AI client reasons over the ZoomInfo tool descriptions and selects the right tool (or chains multiple tools) to fulfill your request. You don't need to specify which tool to use, though you can. New tools published by ZoomInfo are available automatically at the start of each new session. ## Direct Tools Direct tools expose ZoomInfo data to the AI orchestrator. The orchestrator handles multi-step workflows naturally, for example chaining **Lookup → Search → Enrich** when building a list. | Tool | What it does | Credits | |---|---|---| | [Lookup](/docs/mcp/tools/lookup) | Returns valid filter values (industries, regions, job functions) | Free | | [Search Companies](/docs/mcp/tools/search-companies) | Find companies by firmographic criteria | Free | | [Search Contacts](/docs/mcp/tools/search-contacts) | Find contacts by role, company, or signal | Free | | [Enrich Companies](/docs/mcp/tools/enrich-companies) | Full firmographic detail on up to 25 companies | Bulk data | | [Enrich Contacts](/docs/mcp/tools/enrich-contacts) | Full contact detail on up to 25 contacts | Bulk data | | [Find Similar Companies](/docs/mcp/tools/find-similar-companies) | Companies matching a given company's profile | Free | | [Find Similar Contacts](/docs/mcp/tools/find-similar-contacts) | Contacts matching a given contact's profile | Free | | [Find Recommended Contacts](/docs/mcp/tools/find-recommended-contacts) | Outreach-ready contacts for a given account | Free | | [Search Intent](/docs/mcp/tools/search-intent) | Discover companies showing buyer intent signals | Free | | [Enrich Intent](/docs/mcp/tools/enrich-intent) | Fetch intent signals for a specific known company | Bulk data | | [Search Scoops](/docs/mcp/tools/search-scoops) | Discover companies based on real-world business events | Free | | [Enrich Scoops](/docs/mcp/tools/enrich-scoops) | Fetch business-event scoops for a specific known company | Bulk data | | [Enrich News](/docs/mcp/tools/enrich-news) | Fetch categorized news coverage for a specific known company | Bulk data | ## Context Agents Context agents run a sub-agent layer that synthesizes large payloads and returns targeted outputs. They blend ZoomInfo's third-party data with your organization's first-party CRM and conversation history. | Tool | What it does | Credits | |---|---|---| | [Account Research](/docs/mcp/tools/account-research) | Full intelligence briefing on a company | AI action | | [Contact Research](/docs/mcp/tools/contact-research) | Full intelligence briefing on a contact | AI action | ## Roadmap | Capability | What it does | Status | |---|---|---| | GTM Context | Understand who is on the other side of the keyboard: what you sell, who you compete with, your ICP and personas | Early Q2 2026 | | WebSights | Understand who has been visiting your website | Future roadmap | ================================================================================ # ChatGPT > How to connect ZoomInfo MCP to ChatGPT via the app marketplace. Works on all tiers, including free plans. **Date:** 2026-03-27 **Source:** https://gtm.ai/docs/mcp/clients/chatgpt --- ## Overview ZoomInfo is listed in the ChatGPT app marketplace. Once connected, ChatGPT can invoke ZoomInfo tools based on your prompts or when you explicitly ask it to use ZoomInfo. Setup works on all ChatGPT tiers, including free plans. ## Setup **Open the Apps panel** Click **Apps** in the ChatGPT side panel. **Find ZoomInfo** Search for **ZoomInfo** and click **Connect**. **Authenticate** Sign in with your ZoomInfo credentials when prompted. **Verify the connection** Start a new conversation and ask: *"What ZoomInfo tools do you have access to?"* ## Tool Behavior in ChatGPT ChatGPT selects the appropriate ZoomInfo tool based on your request. You can guide tool selection by being specific in your prompts, or by saying "use ZoomInfo to..." followed by your request. You can also prime ChatGPT to use ZoomInfo tools by clicking the **+** in the chat window, hovering over **More**, and selecting **ZoomInfo**. ChatGPT will still reach for ZoomInfo tools without this step, based on conversation context or if you explicitly ask. New ZoomInfo tools become available automatically at the start of each new session. ## Troubleshooting **Can't find the app:** open **Apps** in the side panel and search for "ZoomInfo." If it does not appear, try refreshing the page or starting a new session. **Authentication errors:** confirm you're using your personal ZoomInfo credentials. Admin-only ZoomInfo accounts cannot authenticate to MCP. **Credit errors:** MCP requires bulk data credits. Contact your ZoomInfo admin or ZoomInfo representative if you're unsure whether your account has bulk credits enabled. ================================================================================ # Claude > Step-by-step setup for connecting ZoomInfo MCP to Claude Desktop via the connector marketplace. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/clients/claude --- ## Overview ZoomInfo is listed in the Claude connector marketplace, making setup straightforward. Once connected, Claude can invoke ZoomInfo tools automatically based on your prompts, with no need to specify which tool to use. ## Setup **Open Claude Desktop settings** Go to **Settings → Connectors**. **Find ZoomInfo** Search for ZoomInfo in the connector marketplace and select it. **Authenticate** Sign in with your ZoomInfo credentials when prompted. **Verify the connection** Start a new chat and ask: *"What ZoomInfo tools do you have access to?"* Claude will list all available tools. ## Managing Tool Permissions Claude lets you control which ZoomInfo tools it can invoke. In **Settings → Connectors**, select the ZoomInfo connector to see individual tool toggles. You can enable or disable specific tools (for example, to prevent enrichment calls from consuming credits unexpectedly). By default, Claude will ask for confirmation before invoking a new tool for the first time. ## How Claude Selects Tools Claude reasons over all available ZoomInfo tool descriptions and selects the right tool (or sequence of tools) for your request. You don't need to specify which tool to use, though you can if you want to. For example: - *"Research Acme Corp"* → Claude invokes **Account Research** - *"Find the VP of Sales at companies like Salesforce"* → Claude chains **Find Similar Companies** → **Search Contacts** - *"Enrich these 10 contacts"* → Claude invokes **Enrich Contacts** with up to 25 records per call ## Context Window Behavior Direct tools (Search, Enrich, Lookup) return data into Claude's context window, making it available for follow-up questions within the same conversation. Context agents (Account Research, Contact Research) run a sub-agent layer that synthesizes large payloads before returning a targeted summary. This keeps Claude's context window manageable for complex account research tasks. ## Troubleshooting **Tools aren't appearing:** start a new session. Claude refreshes the tool list at the beginning of each session. Note that Claude defaults to "load tools when needed," only surfacing tools based on the context of the conversation. You can change this to "tools already loaded" to eliminate this as a potential source of the problem: click the **+** in the chat interface, go to **Connectors**, then **Tool Access** to adjust this setting. **Authentication errors:** disconnect and reconnect the ZoomInfo connector in Settings. Make sure you're using your personal ZoomInfo login, not a shared or admin-only account. **Credit errors during enrichment:** confirm your account has bulk data credits enabled. Recurring monthly credits are not supported by MCP. ================================================================================ # Custom Integration > Technical reference for engineering teams building MCP-compatible AI tools that connect to ZoomInfo. **Date:** 2026-05-06 **Source:** https://gtm.ai/docs/mcp/clients/custom --- ## Overview ZoomInfo MCP is an open MCP server. Any client that can complete an OAuth 2.0 Authorization Code flow and speak the MCP protocol can connect to it. This guide walks through what an engineering team needs to build a custom MCP client (or agent) that points at ZoomInfo. ## Server Details | Property | Value | |---|---| | **MCP Server URL** | `https://mcp.zoominfo.com/mcp` | | **Protocol** | Model Context Protocol (MCP) | | **Architecture** | Stateless | | **Auth** | OAuth 2.0 Authorization Code with PKCE | | **Rate limit** | 25 requests per second | ## Create an MCP App in the DevPortal Custom clients authenticate against an MCP App registered in the ZoomInfo Developer Portal. Each tenant manages its own apps; client credentials are issued per app, not per user. **Open the DevPortal** Go to [developer.zoominfo.com](https://developer.zoominfo.com) and sign in. If you don't see the DevPortal in your launcher, your ZoomInfo admin needs to assign the **DevPortal** subscription to your user (Admin Portal → User Management → Users). **Create a new app** Click **Create App** in the top-right corner. A modal appears with two options. Select **MCP App**, not API App. MCP Apps come pre-configured for the Authorization Code flow that MCP clients require. **Configure the app** Provide: - **App name:** something descriptive. Users will see this on the consent screen during sign-in. - **Authentication type:** **Authorization Code**. This is the only supported flow for MCP clients. - **Sign-in redirect URIs:** the callback URL your client will use to receive the authorization code. For local development this is typically `http://localhost:8080/callback`. Production clients should register their public callback URL. You can register multiple URIs on a single app. **Select scopes** Choose the data scopes your tool needs. There is no dedicated MCP scope; pick the underlying datasets the tools you intend to expose will read from (for example, `api:data:company`, `api:data:contact`, `api:data:intent`). Fewer scopes means narrower tool availability at runtime. Full reference: [OAuth 2.0 Scopes](https://docs.zoominfo.com/docs/zoominfo-oauth-20-scopes). **Save and grab credentials** Click **Create**. Internal-use apps are approved immediately and appear in the DevPortal table. Open the app. Copy your **Client ID** and **Client Secret**. Treat the secret like a password: store it in a secrets manager or the OS keychain. Never commit it to source control. If your app is intended for users outside your tenant, see the [Partner App submission process](https://docs.zoominfo.com/docs/app-creation-developer-portal-guide#submitting-a-partner-app). Internal-use apps work for any user inside your ZoomInfo organization without further review. ## Authentication Flow ZoomInfo MCP uses Authorization Code with PKCE. Each end user authenticates with their own ZoomInfo credentials, and tokens are bound to that user. Avoid shared service accounts: tools like Account Research personalize results based on the calling user's CRM context, and a shared identity loses that. The flow your client implements: 1. Generate a PKCE code verifier and challenge. 2. Open the ZoomInfo authorization URL in the user's browser, passing your `client_id`, redirect URI, requested scopes, and the PKCE challenge. 3. The user signs in and approves the requested scopes. 4. ZoomInfo redirects to your callback URL with an authorization code. 5. Your client exchanges the code (plus the PKCE verifier and `client_secret`) at the token endpoint for an access token and refresh token. 6. Use the access token in the `Authorization: Bearer ` header on requests to `https://mcp.zoominfo.com/mcp`. 7. When the access token expires, use the refresh token to get a new one without prompting the user again. Detailed endpoint references and example requests live in the API auth docs: [Authorization Code Flow with PKCE](https://docs.zoominfo.com/docs/authorization-code-flow-with-pkce) and [Refresh Token Flow](https://docs.zoominfo.com/docs/refresh-token-flow). > **Quick testing without building the flow** > > Skip the OAuth handshake while you're prototyping. The DevPortal can mint a short-lived bearer token directly from the app detail page (look for **Generate Bearer Token**). It expires fast. Tokens minted this way consume real credits and count against rate limits. ## Connecting Your Client Most MCP clients accept a server URL plus an OAuth configuration. The exact shape varies by client; the example below is the minimum config form, and your client library will wrap the OAuth handshake described above. ```json { "mcpServers": { "zoominfo": { "url": "https://mcp.zoominfo.com/mcp", "auth": { "type": "oauth2", "clientId": "", "clientSecret": "", "redirectUri": "http://localhost:8080/callback" } } } } ``` On first connection, the client calls `tools/list` to fetch the active set of tools and their schemas. This happens at the start of every session. Your client always sees the latest definitions without versioning overhead. ## Tool Discovery `tools/list` returns every tool the authenticated user has access to. Each definition includes: - **Name:** the identifier passed to `tools/call`. - **Description:** written for LLM routing. It states when to use the tool and what inputs it expects. - **Input schema:** JSON Schema describing required and optional parameters. ```json // Example: tools/list response (abbreviated) { "tools": [ { "name": "lookup", "description": "Reference data for valid search filter values...", "inputSchema": { "type": "object", "properties": { "fieldName": { "type": "string", "description": "Field to lookup (e.g., management-levels, industries)" } }, "required": ["fieldName"] } } ] } ``` ## Calling a Tool Use `tools/call` with the tool name and arguments: ```json { "method": "tools/call", "params": { "name": "search_companies", "arguments": { "industryId": "6374", "employeeCount": { "min": 100, "max": 1000 }, "location": "United States" } } } ``` ## Batch Sizes and Rate Limits | Tool | Max records per call | |---|---| | Enrich Companies | 25 | | Enrich Contacts | 25 | | Find Similar Companies | 25 | | Find Similar Contacts | 25 | | Find Recommended Contacts | 25 | | Account Research | 1 | | Contact Research | 1 | The rate limit is **25 requests per second** per authenticated user. Build retry logic with exponential backoff for production workloads. Bulk extraction and scraping are prohibited under ZoomInfo's Acceptable Use Policy. ## Error Handling ZoomInfo returns standard HTTP status codes. Common ones: | Code | Cause | Resolution | |---|---|---| | `401` | Invalid or expired access token | Refresh the token, or restart the OAuth flow if the refresh token is also expired | | `403` | Insufficient entitlements or missing scope | Check the user's package, bulk credit availability, and that the app was granted the right scopes at creation | | `429` | Rate limit exceeded | Back off and retry | Full error reference: [ZoomInfo API documentation](https://docs.zoominfo.com/reference/overview). ## Context Management Direct tools (Search, Enrich, Lookup) return data straight into the calling LLM's context window. Cap batch sizes to fit your model's window. Research tools (Account Research, Contact Research) run a sub-agent layer that synthesizes large payloads before returning a focused summary. This keeps the calling context manageable even for deep account work. ## API Reference For the full ZoomInfo API and data schema reference, see [docs.zoominfo.com/docs](https://docs.zoominfo.com/docs). ================================================================================ # Microsoft Copilot > How to connect ZoomInfo MCP to Microsoft Copilot via the Copilot Studio marketplace. **Date:** 2026-04-21 **Source:** https://gtm.ai/docs/mcp/clients/microsoft-copilot --- ## Overview ZoomInfo is listed in the Microsoft Copilot Studio marketplace. Once added to an agent, Copilot can invoke ZoomInfo tools automatically based on your prompts, grounding responses in live GTM intelligence. ## Setup **Open your agent in Copilot Studio** Select the agent you want to connect ZoomInfo to. **Add a tool** Click **Add tool** and choose **Model Context Protocol**. **Select ZoomInfo GTM MCP** Scroll to the bottom of the tile list and select **ZoomInfo GTM MCP**. **Authenticate** Sign in with your ZoomInfo credentials when prompted. OAuth is handled automatically. **Verify the connection** Start a new conversation with the agent and ask: *"What ZoomInfo tools do you have access to?"* ## How Copilot Selects Tools Copilot reasons over the ZoomInfo tool descriptions and picks the right tool for each request. You don't need to name the tool. To steer it, be specific in your prompt or say "use ZoomInfo to..." before your request. Ask *"Research Acme Corp"* and Copilot invokes **Account Research**. Ask for VPs of Sales at companies like Salesforce and it chains **Find Similar Companies** into **Search Contacts**. Enrichment calls batch up to 25 records. New ZoomInfo tools become available automatically. Copilot refreshes the tool list at the start of each agent session. ## Troubleshooting **Can't find the tile:** the ZoomInfo GTM MCP tile sits at the bottom of the Model Context Protocol list in Copilot Studio. If it doesn't appear, refresh the page. **Authentication errors:** confirm you're using your personal ZoomInfo credentials. Admin-only ZoomInfo accounts cannot authenticate to MCP. **Tools aren't appearing:** start a new session with the agent. Copilot refreshes the tool list at the beginning of each session. **Credit errors:** MCP requires bulk data credits. Contact your ZoomInfo admin or account team if you're unsure whether bulk credits are enabled. ================================================================================ # Perplexity > How to connect ZoomInfo MCP to Perplexity using its native connector for paid plans. **Date:** 2026-05-06 **Source:** https://gtm.ai/docs/mcp/clients/perplexity --- ## Overview ZoomInfo is a native connector in Perplexity. Paid Perplexity users with access to **Perplexity Computer** can add ZoomInfo from the Connectors menu and route research questions through ZoomInfo's MCP tools alongside Perplexity's own web search. The connector works with whichever model you select inside Perplexity, including Perplexity's default model and third-party options like Anthropic. > **Plan requirement** > > Perplexity Computer is a paid feature. Free Perplexity accounts cannot add the ZoomInfo connector. If you don't see Connectors in your settings, check your subscription with your Perplexity admin. ## Setup **Open Perplexity Settings → Connectors** From the Perplexity web app, go to **Settings**, then **Connectors**. **Search for ZoomInfo** Type "ZoomInfo" in the connector search and select it from the results. **Authenticate** Sign in with your ZoomInfo credentials when prompted. The handshake completes in seconds, and Perplexity returns you to the Connectors screen ready to use ZoomInfo. **Verify the connection** Start a new conversation. ZoomInfo now appears in the connector picker above the input box. Toggle it on for any prompt that should hit ZoomInfo data. ## Using ZoomInfo in a Prompt Once the connector is on, Perplexity invokes ZoomInfo tools live and shows the calls inline. A prompt like: > *Get me 5 contacts for Walmart with a phone number.* will run **Search Contacts** against ZoomInfo and return real records. You can chain follow-ups in the same conversation, and Perplexity carries the context forward. For an account-style question: > *What companies in Boston use Salesforce and have revenue greater than 20 million annually?* Perplexity routes through **Search Companies** with the right filters and returns matching records. ## Tips for Best Results ### Disable web search for pure ZoomInfo answers Perplexity defaults to running its own web search alongside connectors. For prompts that should rely entirely on ZoomInfo (contact lookups, intent signals, account research), untick **Web** in the search options. The connector still runs. ### Be explicit about what you want Perplexity's router sometimes prefers public-domain answers over ZoomInfo when the prompt is ambiguous. Asking *"What does the CEO of Costco care about?"* may pull a mix of public sources and ZoomInfo data. Naming the tool ("research this account in ZoomInfo") or the data type ("pull intent signals for...") biases the router toward the connector. ### Switch models for tougher work From the model picker, you can swap Perplexity's default for Anthropic or another provider. Tool-use behavior is consistent across models. Reasoning and synthesis quality vary. ## How Perplexity Selects Tools Perplexity reads the ZoomInfo tool descriptions and picks the right one for your request. You don't need to specify which tool to call. New tools added to the ZoomInfo MCP server become available automatically; Perplexity refreshes the tool list at the start of each session. ## Troubleshooting **ZoomInfo doesn't appear under Connectors:** confirm you're on a paid Perplexity plan that includes Computer. Free plans don't expose the connector marketplace. **Authentication errors:** make sure you're using your personal ZoomInfo credentials, not a shared or admin-only account. Disconnect and reconnect from **Settings → Connectors** if the token has gone stale. **Mixed results between public web and ZoomInfo:** untick **Web** in the search options for the prompt. Web search runs in parallel by default and can dominate the answer when both sources have something to say. **Credit errors during enrichment:** confirm your account has bulk data credits enabled. Recurring monthly credits are not supported by MCP. ================================================================================ # Credits & Billing > How MCP consumes bulk data credits and AI action credits, how Records Under Management applies, and how admins set limits. **Date:** 2026-03-27 **Source:** https://gtm.ai/docs/mcp/credits --- ## Overview MCP is included with all ZoomInfo subscriptions at no additional cost. Usage counts against your existing credit allocations, the same pools used by the ZoomInfo web app and API. | Credit type | What consumes it | Where to view usage | |---|---|---| | **Bulk data credits** | Enrich tools (Companies, Contacts, Intent) | Admin Portal > Usage > Data Credit Dashboard | | **AI action credits** | Account Research, Contact Research | Admin Portal > Usage > AI Credit Dashboard | Search, Lookup, Find Similar, and Recommended Contacts tools are **free**. > **MCP requires bulk credits** > > MCP does not work with recurring monthly credits. Only bulk data credits are supported. Contact your ZoomInfo admin or ZoomInfo representative if you're unsure whether your account has bulk credits enabled. ## Bulk Data Credits ### How they're charged One bulk data credit is consumed per **new record** enriched. A record is "new" if it hasn't been enriched within the past 12 months under your organization's Records Under Management (RUM). - **Enrich Companies / Contacts / Intent**: 1 credit per new record Whether a credit is charged depends on whether the record is already under management. If a record was previously enriched within the RUM window (by any user, on any ZoomInfo surface), no additional credit is consumed. ### Records Under Management (RUM) Once a record is enriched, it enters RUM for 12 months. Any subsequent enrichment of that same record costs no additional credit within that window. RUM is tracked at the **organization level**, not the user level. ## AI Action Credits Context agents ([Account Research](/docs/mcp/tools/account-research), [Contact Research](/docs/mcp/tools/contact-research)) consume AI action credits proportional to the work done. Credits vary based on query complexity, typically 5 at the lower end and 15 at the upper end. Each call is capped at one account or contact. ## Credit Visibility There is no cost preview shown before a tool call. Here is how credit consumption works in practice: - **Bulk data credits**: may or may not be charged depending on whether the record is already under management via RUM. Enrichment tools process up to 25 records per call, which imposes a natural limit on how many credits a single call can consume. - **AI action credits**: proportional to the inference required for the query. Each call is limited to one account or contact, which bounds the upper end of consumption per call. ## Admin Controls Admins can set bulk data credit limits and AI action credit limits per user in **Admin Portal → Users → User Management**. These limits are respected when users connect through MCP. ## MCP vs. Copilot Credit Model Viewing contact data in Copilot (ZoomInfo's in-product interface) does not consume bulk credits. Enriching via MCP does, because data is being pulled into the AI tool's context (equivalent to an export). Records Under Management ensures you're not charged twice for the same record across both surfaces. ================================================================================ # FAQ & Troubleshooting > Common questions about ZoomInfo MCP and steps to resolve authentication, credit, and compatibility issues. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/faq --- ## General ### Does MCP give me access to more data than my current ZoomInfo package? No. MCP respects your existing package entitlements. You access the same data through your AI tool that you'd access in the ZoomInfo web app. Different packages return different fields, and this is enforced at the API level. ### Can multiple users share a single ZoomInfo login for MCP? This is technically possible but strongly not recommended. Much of the value in context agents like Account Research comes from user-level context: your accounts, your CRM relationships, and your ZoomInfo platform activity. A shared login loses all of that personalization. Each user should authenticate with their own ZoomInfo credentials. ### Does MCP support bulk exports or CRM write-back? No. MCP is designed for interactive AI-assisted workflows. It is not built for bulk data exports, CRM write-back, batch enrichment pipelines, or administrative tasks. For those use cases, use the ZoomInfo web app, REST API, or existing automation integrations. ### When ZoomInfo adds new tools, do I need to do anything? No. New tools are automatically available at the start of your next session. The AI client calls the tool list on each new session and receives an up-to-date set of tools. ### Can I control which ZoomInfo tools the AI is allowed to use? Yes. Most MCP clients let you enable or disable individual tools in the connector settings. You can also influence tool selection by specifying which tool you want in your prompt. ### Is Claude Code (the CLI) supported? Yes. Register the ZoomInfo MCP server with `claude mcp add` after creating an MCP App in the [ZoomInfo Developer Portal](https://developer.zoominfo.com). Full walkthrough in the [Claude Code setup guide](/guides/how-to-connect-to-zoominfo-mcp-in-claude-code). ### Is OpenAI Codex supported? Yes. Add a Custom MCP entry in Codex Settings pointing at `https://mcp.zoominfo.com/mcp` over Streamable HTTP, then complete the ZoomInfo login. Full walkthrough in the [Codex setup guide](/guides/how-to-connect-to-zoominfo-mcp-in-codex). ## Credits ### Why isn't MCP working even though I have a valid ZoomInfo login? MCP requires **bulk data credits** and does not work with recurring monthly credits. If your account only has monthly credits, contact your ZoomInfo admin to enable bulk credits. ### Will I be charged twice if I enrich the same record in MCP and again in Studio? No. Records Under Management (RUM) is respected across all ZoomInfo surfaces. Once a record is enriched anywhere in the platform, it won't be charged again for 12 months regardless of which surface accesses it next. ### Can my admin cap how many credits I can use through MCP? Yes. Admins can set bulk data credit limits and AI action credit limits per user in the Admin Portal. These limits are enforced when users connect through MCP. ### Why was I charged for a record I've already seen in Copilot? Viewing data in Copilot doesn't consume bulk credits. Enriching via MCP does, because the data is pulled into the AI tool's context (equivalent to an export). If the record was already enriched by someone in your organization within the past 12 months, RUM applies and no credit is charged. ## Troubleshooting ### I'm getting a 'Forbidden' error on search or enrich. Try disconnecting and reconnecting your ZoomInfo account in your AI client's connector settings. If the issue persists, contact ZoomInfo support with your user ID. ### I'm getting 401 (unauthorized) errors. Verify you're using a supported AI client and that your ZoomInfo credentials are correct. Try logging out of ZoomInfo in the connector settings and logging back in. Confirm you're using your personal ZoomInfo login, not a shared or admin-only account. ### I can't find ZoomInfo in ChatGPT. ZoomInfo is listed in the ChatGPT app marketplace. Open **Apps** in the side panel and search for "ZoomInfo." If it doesn't appear, try refreshing the page or starting a new session. See the [ChatGPT setup guide](/docs/mcp/clients/chatgpt) for full instructions. ### A tool I expected isn't showing up. Start a new session. Tool lists are refreshed at session start. Ask the model: *"What ZoomInfo tools do you have access to?"* to confirm what's available. If a tool is missing, verify your ZoomInfo package includes the underlying data (e.g., intent data requires Intent Topics to be configured in your subscription). ### What do I do if a query falls outside the scope of available tools? The AI model will tell you. We're also building a feedback mechanism so the model can log requests for functionality not yet available. This feeds directly into our tool roadmap. ================================================================================ # Getting Started > What you need before connecting ZoomInfo to your AI tool. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/getting-started --- ## Requirements Before connecting, confirm you have: - **A ZoomInfo subscription.** Any package (Sales, Copilot, Studio, ZI Lite) qualifies. - **Bulk data credits.** MCP uses bulk data credits, not recurring monthly credits. Contact your admin or ZoomInfo representative if you're unsure whether bulk credits are enabled on your account. - **Your own ZoomInfo login.** Each user must authenticate individually. Admin-only seats (which have no data access by design) cannot use MCP. - **An MCP-compatible AI tool.** See [Clients](/docs/mcp/clients) for setup guides, or build your own with the [Custom Integration](/docs/mcp/clients/custom) guide. > **No specific AI tier required** > > You need a ZoomInfo subscription to authenticate, but no particular tier of Claude, ChatGPT, or other AI tool is required beyond what that platform needs for MCP support. ## MCP Server URL All clients connect to the same endpoint: ``` https://mcp.zoominfo.com/mcp ``` ## Choosing a Client See the [Clients](/docs/mcp/clients) section for step-by-step setup guides: - [Claude](/docs/mcp/clients/claude): listed in the Claude connector marketplace. - [ChatGPT](/docs/mcp/clients/chatgpt): listed in the ChatGPT app marketplace. - [Microsoft Copilot](/docs/mcp/clients/microsoft-copilot): listed in the Copilot Studio marketplace. - [Perplexity](/docs/mcp/clients/perplexity): native connector for paid plans with Computer access. - [Claude Code](/guides/how-to-connect-to-zoominfo-mcp-in-claude-code): Anthropic's CLI, registered via `claude mcp add`. - [Codex](/guides/how-to-connect-to-zoominfo-mcp-in-codex): OpenAI's coding agent, added via Custom MCP settings. - [Custom Integration](/docs/mcp/clients/custom): for engineering teams building their own AI tools. ## Verifying Your Connection Once connected, start a new session and ask: > *"What ZoomInfo tools do you have access to?"* The model will list all available tools from the [Tools reference](/docs/mcp/tools). If tools aren't appearing, try disconnecting and reconnecting your ZoomInfo account in the client's connector settings, and check tool-level permissions on the connector settings page. ## Authentication Model Each user connects using their own ZoomInfo credentials. This is intentional. A large part of what makes tools like Account Research valuable is context scoped to the individual user: their accounts, CRM relationships, and conversation history. **Centralized authentication** (one admin authenticating for all users) is technically possible but not recommended. Any user-level personalization is lost when a shared credential is used. ================================================================================ # Security & Data > How authentication, entitlement enforcement, and data handling work in the ZoomInfo MCP server. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/security --- ## Authentication All requests flow through ZoomInfo's MCP Gateway, which handles authentication, entitlement enforcement, and request routing. Each user authenticates with their own ZoomInfo credentials, the same login used for the ZoomInfo web app. The MCP server is **stateless**. Authentication (who you are) and session state are separate concerns. Each tool call is scoped to the authenticated user, so your account relationships, CRM context, and conversation history are properly associated with your requests. ## Data Entitlements MCP does not grant access to any data beyond what your ZoomInfo package already includes. The data and fields accessible through your AI tool are identical to what you'd access in the ZoomInfo web app. - Different packages return different fields - Some tools are tied to specific entitlements (e.g., Intent data will only be available to organizations with Intent Topics configured) - All tool calls respect your organization's existing package entitlements ## What ZoomInfo Processes ZoomInfo's MCP server processes the tool invocations you make: the parameters you send and the results it returns. ZoomInfo does not have visibility into your broader AI conversations or the context your AI tool maintains between messages. All MCP tools are currently **read-only**. No data is written back to ZoomInfo systems through the MCP connection. ## AI Provider Data Retention When you use ZoomInfo MCP through an AI tool like Claude or ChatGPT, ZoomInfo data passes through that AI provider's infrastructure. Data retention and usage policies for the AI provider are governed by your agreement with that provider, not ZoomInfo. Review your AI provider's data retention settings and disable training data collection where available, particularly for enterprise deployments handling sensitive account or contact data. ## User-Level Access Control Admins can control which users have MCP access via **Admin Portal → Users → User Management**. Toggle the **API Access** setting per user to enable or disable MCP connectivity. There is currently no organization-wide toggle to disable MCP for all users simultaneously. Access is managed at the user level. **Users who cannot use MCP:** - Admin-only seats (intentionally restricted from data access by design) - Users without bulk data credits enabled **Users who can use MCP:** - Any user with a Sales, Copilot, or Studio license with bulk credits enabled ================================================================================ # Account Research > Comprehensive intelligence briefing on a company, blending ZoomInfo third-party data with your CRM and conversation history. Consumes AI action credits proportional to work done. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/account-research --- ## What it does Generates a comprehensive intelligence briefing on a company by synthesizing multiple data sources: - **ZoomInfo third-party data:** firmographics, technographics, hiring signals, funding events, leadership changes, competitive intelligence - **Your CRM data:** account history, open opportunities, current deal stage, past interactions (if your organization has CRM connected to ZoomInfo) - **Conversation intelligence:** key themes, action items, and relationship context from your team's calls and emails with the account (if Conversation Intelligence is enabled) The tool runs a sub-agent that selects and synthesizes the most relevant context, then returns a targeted briefing suitable for meeting prep, account planning, or outreach personalization. ## Credits **AI action credits, proportional to work done.** Each call is limited to one account. The number of credits consumed depends on the complexity of the query, typically ranging from 5 credits at the lower end to 15 at the upper end. ## Architecture Account Research is a context agent. It runs a sub-agent layer rather than returning raw data directly. This serves two purposes: 1. **Context window management:** synthesized briefings fit in a primary orchestrator's context window; raw data dumps from multiple sources would not 2. **Relevance filtering:** the sub-agent selects what's most relevant to the authenticated user and their query ## When the AI uses it The AI invokes Account Research when you ask for a briefing, summary, or research on a specific named company. It is the right tool for "tell me about this account before my call" type prompts. For finding companies matching criteria (without a specific account in mind), use [Search Companies](/docs/mcp/tools/search-companies) instead. ## First-party data requirements CRM and conversation history context in Account Research requires your organization to have connected those sources to ZoomInfo at the tenant level. Contact your ZoomInfo admin to confirm which first-party data sources are connected. ## Example prompts > *"Research Acme Corp before my call tomorrow."* > *"Give me a full briefing on Salesforce. What do we know, what's our relationship history, and what are the key signals right now?"* > *"I have an EBR with HubSpot next week. What should I know?"* ================================================================================ # Contact Research > Comprehensive intelligence briefing on an individual contact, blending ZoomInfo data with CRM and conversation history. Consumes AI action credits proportional to work done. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/contact-research --- ## What it does Generates a comprehensive briefing on an individual contact by synthesizing: - **ZoomInfo contact data:** title, seniority, career history, professional background - **Your CRM data:** relationship history, past interactions, open opportunities linked to this contact - **Conversation intelligence:** key themes, commitments, and relationship signals from your team's calls and emails with this person The tool runs a sub-agent that selects and synthesizes the most relevant context, then returns a targeted briefing suitable for outreach personalization, meeting prep, or understanding a buying committee member. ## Credits **AI action credits, proportional to work done.** Each call is limited to one contact. The number of credits consumed depends on the complexity of the query, typically ranging from 5 credits at the lower end to 15 at the upper end. ## When the AI uses it The AI invokes Contact Research when you ask for a briefing on a specific named individual, such as "tell me about this person before I reach out" type prompts. For finding contacts matching a persona description, use [Search Contacts](/docs/mcp/tools/search-contacts) instead. ## First-party data requirements CRM and conversation history context requires your organization to have connected those sources to ZoomInfo at the tenant level. Contact your ZoomInfo admin to confirm which first-party data sources are connected. ## Example prompts > *"Research Jane Smith, VP of Sales at Acme Corp, before I reach out."* > *"What do I know about John Doe at HubSpot? Has anyone on my team spoken with him?"* > *"Give me a briefing on the CFO at our top renewal account."* ================================================================================ # Enrich Companies > Retrieve full firmographic detail for up to 25 companies per call. Consumes one bulk data credit per new record. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/enrich-companies --- ## What it does Retrieves comprehensive firmographic data for one or more companies, including employee count, revenue, industry classification, headquarters location, tech stack, funding history, and more. Accepts company names, domains, or ZoomInfo company IDs. ## Credits **Bulk data credits:** one credit per new company. Records already under management (enriched within the past 12 months by anyone in your organization) are free to re-enrich. See [Credits & Billing](/docs/mcp/credits). **Batch size:** up to 25 companies per call. ## When the AI uses it The AI invokes Enrich Companies after a Search Companies call when full company detail is needed, or directly when you provide specific company names or domains. It is credit-consuming, so well-behaved models will typically confirm before enriching a large batch. ## What's returned - Full company name and domain - Industry and sub-industry - Employee count and revenue range - Headquarters address - Technology stack - Recent funding and investor data - Hiring velocity and open roles - ZoomInfo company ID for cross-referencing Data returned depends on your ZoomInfo package entitlements. ## Example prompts > *"Enrich these 10 companies: [list]"* > *"Get the full profile for Salesforce, HubSpot, and Outreach."* > *"What's Acme Corp's tech stack and employee count?"* ================================================================================ # Enrich Contacts > Retrieve full contact detail (email, phone, title, seniority) for up to 25 contacts per call. Consumes one bulk data credit per new record. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/enrich-contacts --- ## What it does Retrieves comprehensive contact data for one or more people, including direct email, mobile phone, job title, seniority level, department, and professional history. Accepts names with company context, email addresses, or ZoomInfo contact IDs. ## Credits **Bulk data credits:** one credit per new contact. Records already under management (enriched within the past 12 months by anyone in your organization) are free to re-enrich. See [Credits & Billing](/docs/mcp/credits). **Batch size:** up to 25 contacts per call. ## When the AI uses it The AI invokes Enrich Contacts after a Search Contacts call when full contact detail is needed, or directly when you provide specific names or emails. It is credit-consuming, so well-behaved models will typically confirm before enriching a large batch. ## What's returned - Direct email address - Mobile and direct phone numbers - Job title and department - Management level and seniority - Professional history and education - LinkedIn URL - ZoomInfo contact ID Data returned depends on your ZoomInfo package entitlements. ## Example prompts > *"Get the email and phone for the VP of Sales at Acme Corp."* > *"Enrich these 5 contacts before I send outreach: [list]"* > *"What's the direct dial for Jane Smith at HubSpot?"* ================================================================================ # Enrich Intent > Fetch intent signals for a specific, known company. Consumes bulk data credits. **Date:** 2026-03-27 **Source:** https://gtm.ai/docs/mcp/tools/enrich-intent --- ## What it does Takes a company identifier and returns the full intent profile for that account: which topics they're actively researching, signal trend over time, and intensity score. This is enrichment-oriented rather than discovery-oriented. Useful for mid-funnel account intelligence. Feeds into prep workflows (before a discovery call or renewal conversation, for example) to understand what a known account is currently evaluating. ## Credits **Bulk data credits** per enriched record. See [Credits & Billing](/docs/mcp/credits). ## When the AI uses it The AI invokes this tool when you ask about intent signals for a specific named company. For discovering which companies are showing intent across a topic area, use [Search Intent](/docs/mcp/tools/search-intent) instead. ## Example prompts > *"What is Acme Corp actively researching right now?"* > *"Show me intent signals for Salesforce before my call tomorrow."* > *"What topics is HubSpot evaluating? I want to tailor my renewal pitch."* ================================================================================ # Enrich News > Fetch news articles for a specific known company, categorized by event type. Consumes bulk data credits. **Date:** 2026-04-21 **Source:** https://gtm.ai/docs/mcp/tools/enrich-news --- ## What it does Takes a company identifier and returns news coverage for that account. Articles are mapped to a category taxonomy: financial results, funding, M&A, executive moves, product launches, press releases, and general news. Filter by category and publication date to target the relevant narrative. Where Scoops are the structured research record of what happened, News is the public coverage around it. For outreach messaging and exec briefings, the second usually matters more: it's what the prospect will assume you've read. ## Credits **Bulk data credits** per enriched company. Each article returned counts toward your record limit. See [Credits & Billing](/docs/mcp/credits). ## When the AI uses it The AI invokes this tool when you want recent press coverage for a named company. It pairs with [Enrich Scoops](/docs/mcp/tools/enrich-scoops) for a full "what happened plus how it's covered" briefing. Chain into [Account Research](/docs/mcp/tools/account-research) when the output needs a synthesized written summary. ## Example prompts > *"What's in the news about Stripe this month?"* > *"Pull product launch coverage for Figma in the last 60 days."* > *"Give me the funding and M&A news for my target account list."* ================================================================================ # Enrich Scoops > Fetch business-event scoops for a specific known company. Consumes bulk data credits. **Date:** 2026-04-21 **Source:** https://gtm.ai/docs/mcp/tools/enrich-scoops --- ## What it does Takes a company identifier and returns the Scoops on file for that account: leadership moves, funding, hires, layoffs, product launches, pain points, projects, and related events. Filter by scoop type, topic, department, and date range to scope the briefing to what matters for the current task. This is the structured, categorized replacement for scrolling a company's press page or LinkedIn news feed. It returns the same intelligence in one call, ready to drop into meeting prep or a deal review. ## Credits **Bulk data credits** per enriched company. Each scoop returned counts toward your record limit. See [Credits & Billing](/docs/mcp/credits). ## When the AI uses it The AI invokes this tool when you ask what's happening at a specific named company. For discovering companies by event type across the database, use [Search Scoops](/docs/mcp/tools/search-scoops) instead. Pairs naturally with [Enrich Intent](/docs/mcp/tools/enrich-intent) (what they're researching) and [Enrich News](/docs/mcp/tools/enrich-news) (how it's being covered publicly). ## Example prompts > *"What's changed at Snowflake in the last 90 days?"* > *"Pull every leadership change and project scoop for Datadog this quarter."* > *"Any layoffs or executive exits at my top 20 accounts?"* ================================================================================ # Find Recommended Contacts > Returns AI-ranked contact recommendations at a given account, personalized to your ZoomInfo activity and CRM win patterns. Free to use. **Date:** 2026-03-27 **Source:** https://gtm.ai/docs/mcp/tools/find-recommended-contacts --- ## What it does Surfaces AI-ranked contact recommendations at a specific company, personalized to your ZoomInfo platform activity. When integrated with your CRM, it also draws on your historical win patterns. It answers the question revenue reps face constantly: "Who at this account should I actually talk to?" Recommendations are ranked by relevance to your past engagement patterns and contact reachability (accuracy score). The tool supports three use cases, each pulling from different signals: - **Cold prospecting** based on your ZoomInfo browsing history: which profiles you visit, which contacts you export. The tool surfaces profiles you are most likely to find valuable. - **Deal acceleration** based on contacts associated with closed-won new business in your CRM. - **Renewal and growth** based on contacts associated with expansion wins. Results include full contact details (email, direct dial, title, department) and an explanation of why each person was recommended. An engagement priority section identifies who to contact first, with entry point vs. decision-maker reasoning. ## Credits **Free.** Find Recommended Contacts does not consume bulk data credits. ## When the AI uses it The AI invokes this tool for meeting prep, account planning, or prospecting into a named account when you ask who to reach out to rather than specifying a persona. ## Personalization Recommendations are scoped to the authenticated user. When you authenticate with your own ZoomInfo credentials, this tool draws on your ZoomInfo platform activity (profiles viewed, contacts exported) and CRM win patterns to weight recommendations toward contacts you are most likely to find relevant. This personalization is lost if a shared or centralized credential is used for authentication. ## Example prompts > *"Who should I talk to at Acme Corp?"* > *"I'm heading into a renewal at HubSpot. Who are the key stakeholders I should have on my radar?"* > *"Give me the top 5 contacts to reach out to at each of these 3 accounts."* ================================================================================ # Find Similar Companies > Given a company, returns a list of similar companies matching the same firmographic profile. Free to use. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/find-similar-companies --- ## What it does Takes a company as input and returns a ranked list of companies with a similar profile: same industry, size range, business model, technology footprint, or growth stage. Useful for finding companies similar to your dream customers and building look-alike account lists. ## Credits **Free.** Find Similar Companies does not consume bulk data credits. ## When the AI uses it The AI invokes this tool when you want to find companies similar to a known account, typically for prospecting or expanding a target account list. It may chain into [Enrich Companies](/docs/mcp/tools/enrich-companies) if full detail on the results is needed. ## Example prompts > *"Find 20 companies similar to Salesforce."* > *"What companies look like our best customers? Here's one: [company name]."* > *"I closed a deal with Acme Corp. Find me 10 more just like them."* ================================================================================ # Find Similar Contacts > Given a contact, returns contacts with a similar professional profile (title, function, seniority, and industry). Free to use. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/find-similar-contacts --- ## What it does Takes a contact as input and returns a ranked list of contacts with a similar professional profile, matching on job function, seniority level, industry, and company characteristics. Useful for finding contacts similar to your dream champions. You can scope results to a specific company (for example, "find me the equivalent of John Doe at Company X at Company Y") or leave it unscoped to surface the most similar contacts across different organizations. ## Credits **Free.** Find Similar Contacts does not consume bulk data credits. ## When the AI uses it The AI invokes this tool when you want to find people similar to a known champion, buyer, or successful customer contact, typically to build a target persona list for prospecting. It may chain into [Enrich Contacts](/docs/mcp/tools/enrich-contacts) for full contact details. ## Example prompts > *"Find contacts similar to Jane Smith, our champion at Acme Corp."* > *"I just closed a deal with a RevOps Director at a 500-person SaaS company. Find me 15 more like her."* > *"Who else looks like our best customers' main contacts?"* ================================================================================ # Lookup > Returns valid filter values for use in Search and Enrich calls: industries, regions, job functions, and more. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/lookup --- ## What it does Returns enumerated values with IDs and display names for ZoomInfo filter fields. Call this before Search or Enrich to ensure you're using accepted parameter values. ZoomInfo uses specific enum values that must match exactly. ## Credits **Free.** No credits consumed. ## When the AI uses it The AI invokes Lookup automatically when it needs to resolve a filter value before calling Search Companies or Search Contacts. For example, if you ask to search for contacts in a specific industry or region, the model will call Lookup first to get the correct enum values. ## Parameters | Parameter | Required | Description | |---|---|---| | `fieldName` | Yes | The filter field to look up. Examples: `management-levels`, `job-functions`, `metro-regions`, `industries`, `technologies` | ## Example prompt > *"What industries does ZoomInfo support for searching?"* > *"Find me SaaS companies in the Northeast, and use the right region codes."* The second prompt causes the model to call Lookup for `metro-regions` before calling Search Companies. ================================================================================ # Search Companies > Find companies matching firmographic criteria. Returns a ranked list with basic company data. Free to call. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/search-companies --- ## What it does Searches ZoomInfo's database of 100M+ companies using firmographic filters. Returns a ranked list with basic company data: name, domain, industry, employee count, location, and a ZoomInfo company ID. Search is typically the first step before enrichment. Use the returned company IDs with [Enrich Companies](/docs/mcp/tools/enrich-companies) to retrieve full detail. ## Credits **Free.** No credits consumed. ## When the AI uses it The AI invokes Search Companies when you describe a target account profile rather than a specific company. It may chain [Lookup](/docs/mcp/tools/lookup) first to resolve filter values, then Search Companies to find matching companies, then [Enrich Companies](/docs/mcp/tools/enrich-companies) if full detail is needed. ## Common filters - Industry - Employee count range - Revenue range - Location (country, state, metro region) - Technologies used - Hiring signals ## Example prompts > *"Find SaaS companies with 200–1000 employees headquartered in New York."* > *"What companies in the healthcare industry are using Salesforce?"* > *"Show me fast-growing fintech companies that are currently hiring sales roles."* ================================================================================ # Search Contacts > Find contacts matching role, seniority, company, or signal criteria. Returns a ranked list. Free to call. **Date:** 2026-03-19 **Source:** https://gtm.ai/docs/mcp/tools/search-contacts --- ## What it does Searches ZoomInfo's database of 600M+ contacts using filters like job title, management level, job function, company, location, and more. Returns a ranked list with basic professional data: name, title, company, and a ZoomInfo contact ID. Use the returned contact IDs with [Enrich Contacts](/docs/mcp/tools/enrich-contacts) to retrieve email, phone, and full profile data. ## Credits **Free.** No credits consumed. ## When the AI uses it The AI invokes Search Contacts when you describe a target persona rather than a specific person. It may chain [Lookup](/docs/mcp/tools/lookup) first to resolve filter values, then Search Contacts, then [Enrich Contacts](/docs/mcp/tools/enrich-contacts) if contact details are needed. ## Common filters - Job title or function - Management level (C-suite, VP, Director, Manager, etc.) - Company name or domain - Location - Industry ## Example prompts > *"Find VP-level sales leaders at companies in our target account list."* > *"Who are the IT decision-makers at Acme Corp?"* > *"Find SDRs and BDRs at Series B SaaS companies in California."* ================================================================================ # Search Intent > Discover companies exhibiting buyer intent signals across ZoomInfo's database. Free to use. **Date:** 2026-03-27 **Source:** https://gtm.ai/docs/mcp/tools/search-intent --- ## What it does Takes a set of intent topics or keywords and returns a ranked list of companies currently researching those subjects. Filter by signal strength, industry, company size, and geography to narrow the universe. Output is a list of accounts with their associated intent topics and composite scores. This is a discovery-oriented tool. It surfaces net-new companies entering a buying cycle before they raise their hand, making it useful for top-of-funnel account prioritization. ## Credits **Free.** Search Intent does not consume bulk data credits. ## When the AI uses it The AI invokes this tool when you want to discover which companies are actively researching specific topics. It may chain into [Enrich Intent](/docs/mcp/tools/enrich-intent) for deeper signal detail on a specific account, or into [Search Contacts](/docs/mcp/tools/search-contacts) to find people at high-intent accounts. ## Example prompts > *"Which companies are showing intent signals for data enrichment solutions?"* > *"Find mid-market SaaS companies in the US researching AI-powered sales tools."* > *"What accounts are currently in-market for competitor products like Lusha or Apollo?"* ================================================================================ # Search Scoops > Discover companies based on real-world business events surfaced by ZoomInfo's research team. Free to use. **Date:** 2026-04-21 **Source:** https://gtm.ai/docs/mcp/tools/search-scoops --- ## What it does Searches ZoomInfo's Scoops database for companies matching a business event: leadership changes, funding rounds, layoffs, product launches, pain points, projects, expansions, and more. Filter by scoop type, topic, department, publication date, and the firmographic filter set. Unlike Intent, which is behavioral, Scoops are discrete, human-curated records of things that actually happened. Each one has a date and a source. This is the tool for event-driven prospecting and trigger-based outbound. ## Credits **Free.** Search Scoops does not consume bulk data credits. Each scoop returned counts toward your record and request limits. ## When the AI uses it The AI invokes this tool when you want to find companies based on a recent event rather than firmographic fit. It may chain into [Search Contacts](/docs/mcp/tools/search-contacts) to find the right person at each account, or into [Enrich Scoops](/docs/mcp/tools/enrich-scoops) for a deeper view of a specific company. ## Example prompts > *"Find SaaS companies that hired a new CRO in the last 90 days."* > *"Show me companies with recent pain-point scoops around vendor consolidation."* > *"Which mid-market IT organizations announced a new project in Q1?"* ================================================================================ # Guides ================================================================================ # How to Build Audiences and Lists Inside Claude with ZoomInfo MCP > The complete guide to audience and list building using ZoomInfo's MCP integration inside Claude. Covers basic searches, intent signals, and multi-step research workflows. **Date:** 2025-02-24 **Source:** https://gtm.ai/guides/how-to-audience-building-in-claude --- Audience and list building has traditionally meant toggling between your CRM, a data provider, a spreadsheet, and your email client. ZoomInfo's MCP integration inside Claude collapses all of that into a single conversational workspace. Describe what you're looking for in plain English. Claude executes multi-step research workflows using ZoomInfo's full database, then formats the results however you need them. This guide walks through exactly what's possible, based on the actual tools available inside Claude when ZoomInfo MCP is connected. ## What Is MCP and Why Does It Matter for Audience and List Building? MCP (Model Context Protocol) is an open standard that lets AI assistants like Claude connect directly to external tools and data sources. Connect ZoomInfo's MCP server to Claude and you get real-time access to ZoomInfo's business intelligence database: over 100 million company profiles and 600 million professional contacts. Instead of navigating ZoomInfo's UI to run searches, apply filters, export lists, and then manually analyze the results, you describe what you need. Claude handles tool orchestration behind the scenes: chaining ZoomInfo API calls, applying filters, and delivering results in whatever format works best. The shift: you go from **operating a tool** to **delegating a task**. ## The ZoomInfo Tools Available Inside Claude When the ZoomInfo MCP is connected, Claude gets access to thirteen tools. Understanding the core ones helps you write better prompts and get more out of the integration. ### 1. Lookup (Reference Data) This is the foundation tool. Before Claude can search for "VP-level contacts at mid-market SaaS companies in Boston," it needs to know the exact values ZoomInfo uses for management levels, metro regions, industries, and employee count ranges. The Lookup tool retrieves these standardized values. **What it covers:** - Management levels (C-Suite, VP, Director, Manager, etc.) - Metro regions (e.g., "Boston-Cambridge-Newton, MA-NH") - Industries and industry codes - Employee count ranges - Revenue ranges - Job functions and departments - Technology vendors, products, and categories - Company types, rankings, and more You don't need to know ZoomInfo's internal taxonomy. Just describe what you want in natural language, and Claude translates your intent into the right lookup values before running a search. ### 2. Search Companies Find companies matching specific firmographic criteria. You can filter by industry, location, employee count, revenue, growth rate, funding history, technology stack, and dozens of other attributes. **Example: Search Companies** ``` Find SaaS companies in the Bay Area with 200-500 employees that use HubSpot ``` **Example: Search Companies** ``` Show me Series B startups in fintech with over 50% employee growth in the last year ``` **Example: Search Companies** ``` Find public companies in manufacturing with revenue above $500M ``` ### 3. Search Contacts Find individual professionals by name, title, company, seniority, department, location, and more. This is where targeted list building happens. **Example: Search Contacts** ``` Find VPs of Engineering at cybersecurity companies in New York ``` **Example: Search Contacts** ``` Search for Chief Revenue Officers at companies with 1,000-5,000 employees ``` **Example: Search Contacts** ``` Find marketing directors at companies using Salesforce in the healthcare industry ``` ### 4. Enrich Companies Takes a company you already know about (by name, domain, or ZoomInfo ID) and returns a comprehensive profile: full description, financials, employee count, tech stack, corporate hierarchy, funding history, and contact information. **Example: Enrich Companies** ``` Enrich Stripe, Plaid, and Brex for me. I want to compare their size and funding. ``` **Example: Enrich Companies** ``` Pull the full company profile for snowflake.com ``` ### 5. Enrich Contacts Same concept, but for people. Give Claude an email address, name + company, or ZoomInfo ID and get back a full professional profile including job history, contact details, accuracy scores, and company context. **Example: Enrich Contacts** ``` Enrich these five contacts and tell me which ones have the highest accuracy scores ``` ### 6. Find Similar Companies Provide a reference company and get back a ranked list of up to 100 similar companies, scored by a machine learning model that analyzes industry, revenue, employee count, and other firmographic signals. **Example: Find Similar Companies** ``` I just closed a deal at Snowflake. Find me 20 lookalike accounts. ``` **Example: Find Similar Companies** ``` Show me companies that look like HubSpot but are smaller, under 500 employees ``` ### 7. Find Similar Contacts Provide a reference person and get back contacts with similar profiles. You can optionally constrain results to a specific target company, which is powerful for multi-threading into accounts. **Example: Find Similar Contacts** ``` Who at Amazon looks like the VP of Data Engineering I sold to at Netflix? ``` **Example: Find Similar Contacts** ``` Find contacts similar to our champion at Salesforce, but at Microsoft ``` ### 8. Contact Recommendations Get AI-powered, ranked recommendations of who to contact at a target company, based on your past ZoomInfo activity. Three use cases: ### Prospecting (List Building) Recommendations based on contacts you've previously viewed, copied, or exported on the ZoomInfo platform. ### Deal Acceleration Contacts similar to those in your closed-won CRM opportunities, focused on new business. ### Renewal & Growth Contacts similar to those in closed-won opportunities, focused on expansion. **Example: Contact Recommendations** ``` Who should I reach out to at Snowflake for a prospecting motion? ``` **Example: Contact Recommendations** ``` Based on my past deals, who are the top 10 contacts to target at Microsoft? ``` ### 9. Search Intent Find companies showing active buying intent for specific topics. Each result includes a signal score (60-100) and an audience strength rating (A through E). Powerful when combined with firmographic filters. Layer these to find accounts that match your ICP and are actively in-market. **Example: Search Intent** ``` Find companies with high intent signals for "Data Integration" in the last 30 days, with a signal score above 80 ``` **Example: Search Intent** ``` Which mid-market companies are showing buying intent for CRM software? ``` ### 10. Enrich Intent Get intent signals for a specific company you already know. Provide a company and up to 50 topics, and get back signal scores and audience strength for each. **Example: Enrich Intent** ``` Is Snowflake showing any intent signals for data integration or ETL tools? ``` ### 11. Account Research A high-level research tool that answers natural language questions about a company by combining ZoomInfo data with your CRM and conversation history. Best for call prep, deal reviews, and account orientation. **Example: Account Research** ``` What's going on with Databricks? Give me the full picture before my meeting tomorrow. ``` ### 12. Contact Research The person-level equivalent of Account Research. Combines ZoomInfo profile data with CRM history for meeting prep, outreach planning, or general orientation. **Example: Contact Research** ``` Who is Sarah Chen at Stripe? What's her background and how engaged are we? ``` ## Audience and List Building Workflows: From Prompt to Pipeline Here's where it gets practical. Below are the most common audience and list building workflows and exactly how to execute them inside Claude. ### Build a Targeted Account List **Try this workflow** ``` Find 25 mid-market SaaS companies in the Northeast US with 200-1,000 employees, growing headcount by at least 20% year-over-year, that use Salesforce. ``` Claude will chain together Lookup calls (to resolve "mid-market," "Northeast," "SaaS," employee ranges, and Salesforce as a tech product), then run a Company Search with all the right filters applied. ### Build a Contact List for Outbound **Try this workflow** ``` At each of those companies, find the VP of Sales or Chief Revenue Officer. I need their name, title, company, email, and phone if available. ``` Claude takes the company results from the previous step, runs Contact Searches filtered by management level and job function, and compiles the results. ### Research a Target Account **Try this workflow** ``` I'm preparing for a meeting with Databricks. Give me a full company profile, their tech stack, recent funding, employee growth trends, and recommend the top 5 people I should be talking to there. ``` Claude will run an Enrich Company call, then a Contact Recommendations call, and synthesize everything into a briefing. ### Expand from a Closed Deal (Lookalike List Building) **Try this workflow** ``` We just closed Figma. Find 30 companies that look like Figma, then for each one, find the Head of IT or VP of Engineering. ``` Claude runs Find Similar Companies using Figma as the reference, then iterates through results with Contact Searches. ### Build an Intent-Based Target List **Try this workflow** ``` Find companies showing high buying intent for "Data Integration" in the last 30 days with a signal score above 75. Filter to mid-market companies with 200-1,000 employees. Then find the VP of Data or Head of Engineering at each one. ``` Claude runs Lookup for the intent topic, then Search Intent with firmographic filters, then Contact Searches at each company. This combines behavioral signals (who is actively researching) with firmographic fit (who matches your ICP). ### Multi-thread into an Account **Try this workflow** ``` My main contact at Stripe is Sarah Chen, VP of Data. Find me 5 other people at Stripe with a similar persona I should also be engaging. ``` Claude uses Find Similar Contacts, constraining results to Stripe's company ID, to identify additional stakeholders. ## Using Claude's Output Tools for Audience and List Building Finding the data is half the job. Claude can also format and deliver results in multiple ways, right inside the same conversation. ### Interactive Tables with Artifacts When Claude returns search results, you can ask it to display them as a React artifact with a sortable, filterable table. Especially useful for: - Comparing 20+ companies side-by-side on firmographic attributes - Sorting contacts by accuracy score, seniority, or company size - Scanning a target list before deciding who to reach out to **Try this workflow** ``` Show those results as a sortable table. Include company name, employee count, revenue, industry, and 1-year growth rate. ``` Claude will generate a React component with an interactive data table rendered directly in the conversation. You can sort columns, scan the data visually, and then decide your next move. Artifacts work well for iterative sessions. Ask Claude to "add a column for..." or "filter this down to just companies above $50M revenue" and it regenerates the table in place. ### Spreadsheet Exports When you need to take your list out of Claude and into your CRM, outreach tool, or share it with your team, ask for a spreadsheet. **Try this workflow** ``` Export that contact list as an Excel file with columns for name, title, company, email, phone, and LinkedIn URL. ``` Claude will generate a `.xlsx` file you can download directly. ### Email Drafting Once you've identified your targets and researched their companies, Claude can draft personalized outreach using the context it just gathered. Claude has a built-in message composition tool that generates ready-to-send emails. **Try this workflow** ``` Draft a cold outreach email to Sarah Chen at Stripe. Reference their recent funding round and the data infrastructure team expansion. Keep it under 150 words. ``` Claude generates the email in a dedicated message widget with a subject line, body, and an "Open in Mail" button. For high-stakes outreach, ask for multiple variants with different angles: "one version leading with a pain point, one leading with a peer reference." ### Markdown Reports and Briefings For account research, competitive analysis, or pre-meeting preparation, ask Claude to compile everything into a structured markdown document. **Try this workflow** ``` Write up a one-page account briefing for my meeting with Databricks tomorrow. Include company overview, key contacts, recent news, and recommended talking points. ``` Claude renders this as a clean markdown artifact you can copy, share, or export. ## Prompting Tips for Better Results **Be specific about filters.** Precision helps Claude translate your intent into the right ZoomInfo API parameters. Instead of "big tech companies," say "companies with 5,000+ employees in the Computer Software or Information Technology Services industries." **Chain your requests.** Claude maintains full context within a conversation. Start with a company search, narrow it down, then pivot to contacts. Each step builds on the last: 1. "Find cybersecurity companies in the US with 100-500 employees" 2. "Filter that to just companies with 20%+ headcount growth" 3. "Now find the CISO or VP of Security at each of those" 4. "Enrich the top 10 contacts and show me their accuracy scores" 5. "Export the ones with accuracy scores above 85 as a spreadsheet" 6. "Draft a personalized email for the top 3" **Use batch operations.** Enrich Companies and Enrich Contacts both support batches of up to 10 per call. Give Claude a list instead of asking one at a time. **Ask for recommendations over searches when possible.** If your ZoomInfo account has usage history, Contact Recommendations is often more valuable than a raw search. It uses your past engagement patterns to surface contacts you're statistically more likely to convert. **Layer intent signals on firmographic searches.** Use Search Intent to find companies actively researching your category, then filter by firmographic fit. Accounts that match your ICP and are actively in-market convert at higher rates. **Combine ZoomInfo data with web search.** Claude also has web search capabilities. You can combine ZoomInfo's structured data with real-time web intelligence: **Example: Combined Workflow** ``` Enrich Anthropic from ZoomInfo, then search the web for any recent product announcements or funding news from the last 30 days. Compile it all into a pre-call brief. ``` ## Example: Full List Building Session Here's what a real list building session looks like, end to end: **Define your target audience** **You:** "I sell data integration tools to mid-market companies. Find 20 companies in the data engineering space with 200-1,000 employees that use Snowflake." *Claude runs Lookup for industries, employee ranges, and Snowflake as a tech product. Runs Search Companies with all filters applied. Returns 20 companies with names, employee counts, revenue, and locations.* **Visualize the results** **You:** "Great. Show that as a sortable table and add their 1-year growth rate." *Claude generates a React artifact with an interactive table. Columns: company name, industry, employees, revenue, location, growth rate. All sortable.* **Find your buyers** **You:** "Love it. Now find the VP of Data Engineering or Head of Data Infrastructure at the top 10 by growth rate." *Claude runs Contact Searches across 10 companies, filtering for relevant titles and management levels. Returns contact names, titles, emails, and accuracy scores.* **Enrich and activate** **You:** "Enrich the top 5 by accuracy score and draft a personalized outreach email for each." *Claude runs batch Enrich Contacts. Then generates 5 personalized emails using the message compose tool, each referencing the contact's company context, tech stack, and growth trajectory.* **Export for your stack** **You:** "Export all the contacts as a spreadsheet I can upload to my outreach tool." *Claude generates a .xlsx file with all contact details, ready for download.* That entire workflow, which would traditionally involve 30-60 minutes of clicking between ZoomInfo, a spreadsheet, and an email drafting tool, takes a few minutes of conversation. ## Getting Started **Check your requirements** You need a ZoomInfo subscription with bulk data credits and your own ZoomInfo login. See the full [requirements](/docs/mcp/getting-started). **Connect ZoomInfo MCP in Claude** Go to **Settings → Connectors** in Claude, find ZoomInfo, and authenticate with your credentials. The [Claude setup guide](/docs/mcp/clients/claude) has the full walkthrough. **Start prompting** Use any of the workflows described above. No API keys to manage, no code to write. Describe the audience or list building task in natural language. Claude figures out which tools to call, in what order, and how to combine the results. ## Takeaways ZoomInfo's MCP integration turns Claude into a full audience and list building workstation. The thirteen tools cover the entire lifecycle: account identification, intent-based prioritization, contact discovery, enrichment, and personalized outreach. Combined with Claude's native ability to generate interactive tables, spreadsheets, email drafts, and research documents, you get a workflow that's faster, more flexible, and more intelligent than switching between point solutions. The biggest mental shift: stop thinking about which buttons to click and start thinking about what outcomes you want. Describe the end state, and let Claude figure out the steps. ================================================================================ # How to Connect ZoomInfo to Claude Code Using MCP > Set up the ZoomInfo MCP server inside Claude Code using a developer app, the claude mcp add command, and an OAuth handshake on localhost. **Date:** 2026-04-21 **Source:** https://gtm.ai/guides/how-to-connect-to-zoominfo-mcp-in-claude-code --- Claude Code is Anthropic's CLI for agentic coding. If you work in the terminal, you can give it the same ZoomInfo tools that ship with Claude Desktop: search, enrichment, similar companies, intent, research. The setup is a little different from Claude Desktop because Claude Code doesn't have a connector marketplace. You register the ZoomInfo MCP server with a one-line command, then authenticate through a browser callback. This guide walks through the full setup: creating an MCP app in the ZoomInfo Developer Portal, wiring it into Claude Code, and verifying the connection. ## What You Need Before You Start **Claude Code installed.** If you don't have it yet, follow Anthropic's [install instructions](https://docs.anthropic.com/en/docs/claude-code/overview). Any recent version supports MCP. **A ZoomInfo subscription with bulk data credits.** Any ZoomInfo package qualifies, but your account must have bulk data credits enabled. Recurring monthly credits are not supported by MCP. Check with your ZoomInfo admin if you're unsure. See the full [requirements](/docs/mcp/getting-started). **Access to the ZoomInfo Developer Portal.** You'll create an MCP app there to generate the client credentials Claude Code needs. If you can't sign in, your ZoomInfo admin can enable DevPortal access on your seat. **Port 8080 available on localhost.** The OAuth callback runs on `http://localhost:8080/callback` during the initial handshake. If port 8080 is already in use, either free it or pick a different port and match it in the steps below. ## Step-by-Step Setup ### 1. Create an MCP App in the Developer Portal Go to [developer.zoominfo.com](https://developer.zoominfo.com) and click **Create App** in the top right. In the modal, select **MCP App** and fill in: - **Application name:** anything that helps you recognize it later (e.g. *claude-code-local*). - **Authentication type:** **Authorization Code**. - **Redirect URI:** `http://localhost:8080/callback`. Click **Create**. Your app appears in the DevPortal table. ### 2. Copy Your Client Credentials Open the app and copy the `client_id` and `client_secret`. Treat the secret like a password: paste it directly into the next command or stash it in a password manager. Do not commit it, log it, or leave it in plaintext on disk. ### 3. Register ZoomInfo with Claude Code From your shell, run: ```bash claude mcp add --transport http \ --callback-port 8080 \ --client-id \ --client-secret \ zoominfo https://mcp.zoominfo.com/mcp ``` Replace `` with the value from the DevPortal. The `--client-secret` flag with no value tells Claude Code to prompt for the secret interactively, so it's never stored in your shell history. A few notes: - `--transport http` selects HTTP transport. ZoomInfo's MCP server does not use stdio. - `--callback-port 8080` has to match the redirect URI you registered in step 1. Change both together if you need a different port. - `zoominfo` is the local name Claude Code will use to reference this server. Pick whatever name you want; the guide assumes `zoominfo`. ### 4. Initialize the Connection Inside a Claude Code session, run: ``` /mcp ``` Claude Code opens your default browser to the ZoomInfo login page. Sign in with ZoomInfo credentials or SSO, wait for the redirect back to localhost, and watch the terminal for a confirmation that the `zoominfo` server is connected. Close the browser tab when you're done. That's it. All ZoomInfo tools are now exposed to Claude Code. ## Verifying the Connection Works Try a question that needs ZoomInfo data: ``` How many employees does Snowflake have? ``` or: ``` Look up the CEO of Datadog. ``` If the connector is healthy, Claude Code calls the matching ZoomInfo tool and returns live data. You'll see the tool invocation and arguments inline. If Claude says it can't reach ZoomInfo, the server probably hasn't finished authenticating. Re-run `/mcp` and check the status output. For a broader smoke test, chain tools together: ``` Find 5 cybersecurity companies in New York with 200-500 employees, then find the VP of Engineering at each one. ``` That exercises Lookup, Search Companies, and Search Contacts in sequence. To confirm intent data access: ``` Are any companies showing buying intent for "Data Integration" with a signal score above 80? ``` ## Working With Tools in Claude Code Claude Code selects tools the same way Claude Desktop does: it reads the MCP tool descriptions and picks the right one for your request. You don't need to name the tool. But the CLI context has a few things worth knowing. **The /mcp command is your status panel.** Run `/mcp` any time to see which servers are connected, which tools are exposed, and the current auth state. This is the fastest way to debug a broken connection. **Tool results land in your terminal context.** Direct tools (Search, Enrich, Lookup) return data into Claude Code's context window. Large result sets eat context quickly, so narrow filters before running a search over thousands of companies. **Research tools run sub-agents.** Account Research and Contact Research synthesize large payloads before returning a summary. They cost more credits per call, but they keep your context window manageable for work that spans multiple accounts. **Bulk credits apply the same way.** Enrichment tools batch up to 25 records per call. Each record consumed counts against your bulk credits regardless of whether Claude Code or Claude Desktop made the call. ## Troubleshooting **`/mcp` never opens the browser.** Check that port 8080 is free. Run `lsof -i :8080` to see what's holding it. If another process owns the port, either stop it or re-register with a different `--callback-port`. The redirect URI in your DevPortal app has to match the new port. **"Invalid client" or "redirect_uri mismatch" during auth.** The redirect URI in your DevPortal app must match exactly: `http://localhost:8080/callback`, lowercase, no trailing slash. Mismatches here are the most common cause of a failed handshake. **Claude Code doesn't see any ZoomInfo tools.** Run `/mcp` and confirm the `zoominfo` server shows as connected. If it's listed but shows an auth error, re-run the slash command to re-trigger the OAuth flow. **The connection stops working after a few hours.** Your ZoomInfo session may have expired. Re-run `/mcp` to refresh tokens. **Credit errors on enrichment or research calls.** MCP requires bulk data credits. Confirm with your ZoomInfo admin that your account has bulk credits enabled, not recurring monthly credits. ## What's Next With the connector live, Claude Code can pull account lists, enrich contacts, and prep deals in the same session you're writing code. For a deeper walkthrough of audience building, see [How to Build Audiences and Lists Inside Claude](/guides/how-to-audience-building-in-claude). For the full tool reference, see the [MCP Tools documentation](/docs/mcp/tools). ================================================================================ # How to Connect ZoomInfo to Claude Using MCP > Step-by-step setup guide for connecting ZoomInfo's MCP integration to Claude. Covers authentication, verification, and troubleshooting. **Date:** 2025-02-24 **Source:** https://gtm.ai/guides/how-to-connect-to-zoominfo-mcp-in-claude --- ZoomInfo is available as a native MCP connector inside Claude. Once connected, you can search ZoomInfo's database, enrich companies and contacts, find lookalike accounts, and get AI-powered contact recommendations, all through natural language conversation. No API keys, no code, no browser extensions. This guide walks through setup, what you get once connected, and how to verify everything is working. ## What You Need Before You Start **A ZoomInfo subscription with bulk data credits.** Any ZoomInfo package qualifies (Sales, Copilot, Studio, or ZI Lite), but your account must have bulk data credits enabled. MCP does not work with recurring monthly credits. Check with your ZoomInfo admin if you're unsure. See the full [requirements](/docs/mcp/getting-started). **Your own ZoomInfo login.** Each user authenticates individually. Admin-only seats (which have no data access by design) cannot use MCP. The data you can access through Claude matches your ZoomInfo entitlements, so if your seat includes contact emails and direct dials, those will be available in Claude too. **Claude with MCP support.** Claude needs to support connectors for this integration. See the [Claude client setup guide](/docs/mcp/clients/claude) for specifics. ## Step-by-Step Setup ### 1. Open Claude Settings Go to **Settings → Connectors** in Claude Desktop. ### 2. Find and Enable ZoomInfo Search for ZoomInfo in the connector marketplace and select it. ZoomInfo is listed as a native integration. ### 3. Authenticate Claude will redirect you to ZoomInfo's authentication flow. Sign in with your ZoomInfo credentials and authorize the connection. This is a standard OAuth handshake granting Claude permission to make API calls to ZoomInfo on your behalf. ### 4. Confirm the Connection Start a new chat and ask: *"What ZoomInfo tools do you have access to?"* Claude will list all available tools. If they appear, setup is complete. For the full walkthrough with screenshots and troubleshooting, see the [Claude client setup guide](/docs/mcp/clients/claude). ## What You Get Once Connected With the ZoomInfo connector active, Claude gains access to thirteen tools that cover the full audience building, research, and intent data lifecycle. Here's what each one does and when Claude uses it. ### Lookup Retrieves standardized reference values from ZoomInfo's taxonomy. This includes management levels (VP, Director, C-Suite), metro regions, industries, employee count ranges, revenue ranges, job functions, departments, technology vendors and products, and more. You'll never need to call this directly. Claude uses it behind the scenes to translate your natural language requests into exact ZoomInfo filter values. When you say "mid-market SaaS companies in Boston," Claude looks up the right metro region name, industry codes, and employee count ranges before running your search. Lookup covers management levels, metro regions, industries, employee counts, revenue ranges, job functions, departments, technology vendors and products, intent topics, buying groups, board members, news categories, company rankings, and more. ### Search Companies Finds companies matching firmographic criteria. Filters include industry, location, employee count, revenue, growth rate, funding history, technology stack, company type, and dozens more. ### Search Contacts Finds individual professionals by name, title, company, seniority, department, location, education, and other attributes. This is the core list building tool. ### Enrich Companies Takes a company you already know (by name, domain, ticker, or ZoomInfo ID) and returns a full profile: business description, financials, employee count, tech stack, corporate hierarchy, funding history, and headquarters information. Supports batch enrichment of up to 10 companies per call. ### Enrich Contacts Takes a contact you already know (by email, name + company, phone, or ZoomInfo ID) and returns their full professional profile including job title, company details, contact information, accuracy scores, and employment history. Also supports batches of up to 10. ### Find Similar Companies Provide a reference company and get back up to 100 lookalike companies, ranked by a machine learning model that analyzes industry, revenue, employee count, and other firmographic signals. This is how you expand from a successful account into a target list of similar ones. ### Find Similar Contacts Provide a reference person and get back contacts with similar professional profiles. You can optionally constrain results to a specific target company, which is useful for finding the right stakeholders at a new account based on a buyer persona you already know. ### Contact Recommendations Returns AI-ranked recommendations of who to contact at a target company based on your ZoomInfo usage history. Supports three modes: prospecting (based on your past views, exports, and copies), deal acceleration (based on closed-won CRM contacts for new business), and renewal and growth (based on closed-won contacts for expansion). ### Account Research A high-level research tool that combines ZoomInfo market data with your CRM and conversation history to answer natural language questions about a company. Ask "What's going on with this account?" or "Prepare me for a meeting with their VP of Engineering" and Claude synthesizes company context, relationship status, deal history, and recent developments into a briefing. Best for call prep, deal reviews, and general account orientation. ### Contact Research The person-level equivalent of Account Research. Give Claude a contact and ask about their background, role, career history, or your relationship with them. Claude combines ZoomInfo profile data with CRM history to produce meeting prep, outreach planning context, or general orientation on who someone is and why they matter to your deal. ### Search Intent Finds companies showing buying intent for specific topics across ZoomInfo's intent signal database. Each signal includes a score (60-100) indicating interest level and an audience strength rating (A through E) showing how many people at the company are researching the topic. Combine intent signals with firmographic filters to build audiences of companies actively researching your category. ### Enrich Intent Fetches intent signals for a specific company you already know. Provide a company identifier and up to 50 intent topics, and get back signal scores and audience strength for each. Use this to prioritize outreach timing or validate whether a target account is actively in-market. ### Submit Feedback A utility tool for sending feedback to ZoomInfo about data quality, feature requests, or access issues directly from within Claude. ## Verifying the Connection Works The simplest way to confirm everything is working: ask Claude a question that requires ZoomInfo data. Try something like: ``` How many employees does Snowflake have? ``` or ``` Look up the CEO of Datadog ``` If the connector is working, Claude will call the appropriate ZoomInfo tool and return live data. You'll see the tool being invoked in the conversation. If something isn't connected properly, Claude will let you know it can't access ZoomInfo and you can revisit the connector settings. For a more thorough test, try a multi-step request: **Multi-step test** ``` Find 5 cybersecurity companies in New York with 200-500 employees, then find the VP of Engineering at each one. ``` This exercises Lookup, Search Companies, and Search Contacts in sequence. All three returning results confirms the connection. To test intent data, try: ``` Are any companies showing buying intent for "Data Integration" with a signal score above 80? ``` This exercises Lookup (to resolve the intent topic) and Search Intent. Signal scores and audience strength ratings in the results confirm your intent data access is working. ## How Authentication and Permissions Work A few things worth knowing about the security model: **Your ZoomInfo entitlements carry over.** Claude can only access data that your ZoomInfo account is entitled to. If your seat doesn't include direct dial phone numbers, Claude won't be able to surface them either. The connector doesn't grant any additional data access beyond what you already have. **The connection persists across sessions.** Once you authenticate, you don't need to re-authenticate every time you start a new conversation. The connector stays active until you explicitly disconnect it. **You can disconnect at any time.** If you want to revoke Claude's access to ZoomInfo, go back to the connectors store and disable the integration. This immediately terminates the connection. ## Tips for Getting the Most Out of the Integration **You don't need to know ZoomInfo's filter taxonomy.** Describe what you want in plain language. "Series B fintech companies in the Southeast with fast headcount growth" is a perfectly good prompt. Claude handles the translation into ZoomInfo's internal field values. **Chain your requests in a single conversation.** Claude maintains context across messages. Start with a company search, narrow it down, pivot to contacts, enrich the best ones, and draft outreach, all in one thread. Each step builds on the last. **Combine ZoomInfo with Claude's other capabilities.** The ZoomInfo connector is one of several tools Claude can access simultaneously. Combine ZoomInfo data with web search for recent news, use artifacts to build interactive comparison tables, export results as spreadsheets, or draft personalized emails. All within the same conversation. **Use intent data to time your outreach.** Search Intent and Enrich Intent let you identify accounts actively researching your category. Layer intent signals on top of firmographic searches to prioritize accounts that are both a good fit and actively in-market. **Use batch operations for efficiency.** Both enrichment tools support up to 10 records per call. Instead of asking Claude to enrich contacts one at a time, give it a list. ## Troubleshooting **"I asked a ZoomInfo question but Claude didn't use the tool."** Make sure the connector shows as active in your settings. If it was recently enabled, try refreshing the page or starting a new conversation. **"Claude says it can't find any results."** This usually means the search criteria are too narrow. Try broadening your filters. For example, expand the employee count range or remove one of the location constraints. Claude will tell you what filters it applied, so you can see what to adjust. **"I'm getting fewer fields than expected."** The fields returned depend on your ZoomInfo subscription tier. If you're missing contact emails or phone numbers, check with your ZoomInfo admin to confirm your entitlements include those data points. **"The connection stopped working."** Your ZoomInfo session token may have expired. Disconnect and reconnect the integration from the connectors store to re-authenticate. ## What's Next Start with a real task you'd normally do in ZoomInfo's UI. Build an account list, research a company before a call, or find contacts at a target account. Same data, accessed conversationally. For a deeper walkthrough of specific workflows, see our guide on [How to Build Audiences and Lists Inside Claude with ZoomInfo MCP](/guides/how-to-audience-building-in-claude). For the full tool reference, see the [MCP Tools documentation](/docs/mcp/tools). ================================================================================ # How to Connect ZoomInfo to Codex Using MCP > Set up the ZoomInfo MCP server inside OpenAI Codex by adding a Custom MCP entry pointing at https://mcp.zoominfo.com/mcp. **Date:** 2026-05-07 **Source:** https://gtm.ai/guides/how-to-connect-to-zoominfo-mcp-in-codex --- Codex is OpenAI's coding agent, powered by ChatGPT. It supports MCP through a Custom MCP entry in its settings. Once connected, Codex can call the same ZoomInfo tools that ship with Claude Desktop and ChatGPT: search, enrichment, similar companies, intent, research. Setup is short. There is no DevPortal app, no OAuth callback to host, and no CLI command. You paste the ZoomInfo MCP URL into Codex's Custom MCP form and complete the ZoomInfo login. ## What You Need Before You Start **A Codex account with Custom MCP support.** OpenAI exposes MCP as a Custom MCP form in Codex Settings. If you don't see it, your plan or workspace may not have it enabled yet. **A ZoomInfo subscription with bulk data credits.** Any ZoomInfo package qualifies, but your account must have bulk data credits enabled. Recurring monthly credits are not supported by MCP. Check with your ZoomInfo admin if you're unsure. See the full [requirements](/docs/mcp/getting-started). ## Step-by-Step Setup **Open Codex Settings** From inside Codex, go to **Settings**, then **Custom MCP**. **Add a new MCP server** Click **Add** (or the equivalent new-entry button) and fill in: - **Name:** `ZoomInfo` - **Transport:** **Streamable HTTP** - **URL:** `https://mcp.zoominfo.com/mcp` **Save and authenticate** Click **Save**. Codex prompts you to sign in with ZoomInfo. Use your normal ZoomInfo credentials or SSO. **Verify the connection** Start a new Codex session and ask: *"What ZoomInfo tools do you have access to?"* Codex lists the tools it sees from the MCP server. ## Verifying the Connection Works Try a question that needs ZoomInfo data: ``` How many employees does Snowflake have? ``` or: ``` Look up the CEO of Datadog. ``` If the connector is healthy, Codex calls the matching ZoomInfo tool and returns live data. The tool invocation appears inline. For a chained smoke test: ``` Find 5 cybersecurity companies in New York with 200-500 employees, then find the VP of Engineering at each. ``` That exercises Lookup, Search Companies, and Search Contacts in sequence. ## Working With Tools in Codex Codex selects tools the same way other MCP clients do: it reads the tool descriptions and picks the right one for your request. You don't need to name the tool. A few things worth knowing for the coding-agent context: **Tool results land in your session context.** Direct tools (Search, Enrich, Lookup) return data into Codex's context window. Large result sets eat context fast, so narrow filters before running a search across thousands of records. **Research tools run sub-agents.** Account Research and Contact Research synthesize large payloads before returning a summary. They cost more credits per call but keep your context manageable. **Bulk credits apply the same way.** Each enrichment record consumed counts against your bulk credits regardless of which client made the call. ## Troubleshooting **Codex doesn't show a Custom MCP option.** Your Codex plan or workspace may not have Custom MCP enabled. Check with your OpenAI admin or workspace owner. **Authentication fails on Save.** Confirm you're signing in with your personal ZoomInfo credentials, not a shared or admin-only account. If your ZoomInfo session has expired, the connection won't complete. **Codex doesn't see any ZoomInfo tools.** Open Settings → Custom MCP and confirm the entry is listed and the URL is exactly `https://mcp.zoominfo.com/mcp`. Remove and re-add the entry if the auth state looks stale. **Credit errors on enrichment or research calls.** MCP requires bulk data credits. Confirm with your ZoomInfo admin that your account has bulk credits enabled, not recurring monthly credits. ## What's Next With ZoomInfo wired into Codex, you can pull account lists, enrich contacts, and prep deals in the same session you're writing code. For the full tool reference, see the [MCP Tools documentation](/docs/mcp/tools). For the equivalent setup in Anthropic's CLI, see [How to Connect ZoomInfo to Claude Code Using MCP](/guides/how-to-connect-to-zoominfo-mcp-in-claude-code). ================================================================================ # Model Choice for GTM AI: What Actually Matters > A research-backed guide to AI model selection for go-to-market teams. Covers the latest frontier models, benchmark data, the commoditization thesis, and a practical framework for choosing the right model for each GTM workflow. **Date:** 2026-03-20 **Source:** https://gtm.ai/guides/model-choice-for-gtm-ai --- When a new model drops, the discourse follows a predictable pattern. Benchmark tables. Head-to-head comparisons. Blog posts declaring a new winner. For teams building GTM AI workflows, the question is more practical: does model choice actually matter for the outcomes you care about? The short answer is layered: **model choice matters less than context quality, matters somewhat for specific task categories, and is converging fast across the frontier.** The longer answer requires understanding benchmark data, where real differences persist, and how routing decisions affect your costs and outputs. This guide covers the current frontier model field, what the research says about benchmark convergence, where model differentiation persists, and a practical framework for GTM model selection. ## The Current Frontier Models As of March 2026, every major AI provider has at least one model capable of handling every standard GTM task at high quality. The deciding factors are cost, context window, and performance on your specific task profile. ### OpenAI **GPT-5.4 (xhigh)** is OpenAI's most capable model as of March 2026. Released March 5, it features a 1.1 million token context window. Artificial Analysis Intelligence Index score: 57 (vs. median 31 for comparable models). GPQA Diamond: ~97%. Pricing: $2.50/1M input, $15.00/1M output. **GPT-5**, released August 2025, operates at $1.25/1M input and $10.00/1M output with a 400K token context window. SWE-bench Verified coding score of 74.9%. Positioned as the general-purpose flagship for most professional use cases. **GPT-4.1**, released April 2025, is OpenAI's workhorse: $2.00/1M input, $8.00/1M output, 1 million token context window. Stronger instruction-following and coding than GPT-4o, at a lower price. The recommended default for high-volume professional tasks. **o3 and o4-mini** are OpenAI's reasoning model line. o3 at $2.00/$8.00 targets complex multi-step reasoning. o4-mini at $1.10/$4.40 delivers strong coding and math performance at the budget tier. o1 (the original reasoning model at $15.00/$60.00) has largely been supplanted; o3 offers comparable reasoning at 87% lower cost. **GPT-4o mini** remains in production at $0.15/$0.60, the default for high-volume, lower-complexity tasks where cost efficiency is the primary constraint. ### Anthropic **Claude Opus 4.6**, released February 5, 2026, is Anthropic's flagship. GPQA Diamond: ~95%. ARC-AGI-2: 68.8%. As of [March 13, 2026](https://claude.com/blog/1m-context-ga), the full 1M token context window is generally available at standard pricing: $5.00/$25.00 with no long-context premium. **Claude Sonnet 4.6**, released February 17, 2026, is Anthropic's mid-tier workhorse. Priced at $3.00/$15.00 across the full 1M context window with no surcharge. It scores 74.1% on GPQA Diamond and 79.6% on SWE-bench Verified, with a 27-point math benchmark jump over Sonnet 4.5. Strong computer use capabilities make it a fit for agentic GTM workflows. **Claude Haiku 4.5** covers the fast/cheap tier at $0.80/$4.00 with a 200K token context window. ### Google Google's Gemini line has delivered the most aggressive price-to-performance ratio across the frontier. **Gemini 3.1 Pro** (Preview, February 2026) offers a 1M context window at $2.00/$12.00 ($4.00/$18.00 above 200K tokens). It scores 94.3% on GPQA Diamond, 80.6% on SWE-bench Verified, and 77.1% on ARC-AGI-2. That ARC-AGI-2 score leads all models. Three configurable thinking levels (low, medium, high) let you balance speed against reasoning depth. **Gemini 3 Pro** (November 2025): Same pricing structure as 3.1 Pro. GPQA Diamond 91.9%, SWE-bench Verified 76.2%. Introduced configurable thinking levels. **Gemini 2.5 Flash**: 1M token context window at $0.30/$2.50. The price-performance standout for mid-tier GTM workflows, offering a 1M context window at a fraction of the cost of comparable options from other providers. **Gemini 2.5 Flash-Lite**: 1M token context window at $0.10/$0.40. The lowest-cost 1M context model available. ### Meta (Open Weight) **Llama 4 Scout** (April 2025) holds the largest context window of any released model: 10 million tokens. MoE architecture, 17B active parameters out of 109B total. Open weights mean zero per-token API fees. Organizations running their own inference pay only infrastructure costs. **Llama 4 Maverick** (April 2025): 1M token context window, 17B active / 400B total parameters MoE. Strong benchmark performance. Open weights. **Llama 4 Behemoth** (288B active / ~2T total parameters) remains in training as of March 2026. It has [not been publicly released](https://www.computerworld.com/article/3987990/meta-hits-pause-on-llama-4-behemoth-ai-model-amid-capability-concerns.html). Meta uses it as a teacher model for distilling Scout and Maverick. Llama 4 changed the open-source calculus significantly. Organizations with sufficient infrastructure can run frontier-class performance at zero marginal token cost. ### Mistral **Mistral Large 3** (December 2025): Sparse Mixture of Experts with 41B active parameters and 675B total. 256K context window at $0.50/$1.50. Open weights under Apache 2.0. **Mistral Small 4** (March 2026): 256K context window, 119B parameters, open-source. Hybrid model unifying instruct, reasoning, and coding with configurable reasoning depth. ### DeepSeek and Others **DeepSeek V3/R1** (January 2025) achieved GPT-4-class performance at $0.028/1M tokens, up to 27x cheaper than premium proprietary models. Open weights. Extended context up to 2M tokens. The release remains the most cited data point in the commoditization debate. DeepSeek V4 and R2 have been [announced](https://www.pymnts.com/artificial-intelligence-2/2026/deepseek-poised-to-unveil-latest-ai-model/) but remain unreleased as of March 2026. **Grok 4.1** from xAI: $0.20/$0.50 with a 2M token context window. Aggressive pricing in the low-cost frontier tier. --- ## The Benchmark Picture Benchmarks are how the industry measures model capability. Increasingly, they are an unreliable guide to real-world performance. Understanding why matters for model selection. ### The Saturation Problem The research community has documented benchmark saturation extensively. A February 2026 paper, ["When AI Benchmarks Plateau"](https://arxiv.org/abs/2602.16763), provides systematic analysis of the phenomenon. The data is striking. **Standard MMLU** (Massive Multitask Language Understanding): Top frontier models now consistently exceed 88% accuracy across 57 academic subject areas. The benchmark was designed to distinguish between models. It no longer can. The [Stanford AI Index 2025](https://aiindex.stanford.edu/report/) explicitly identified MMLU as unable to differentiate between leading models. **GSM8K** (grade school math): GPT-5.3 Codex scores 99%. Fully saturated. Useless for frontier comparison. **HumanEval** (coding): Many frontier models now exceed 87-90%. Approaching saturation. **Chatbot Arena Elo convergence:** The gap between top and 10th-ranked models fell from 11.9% to 5.4% in one year. Between the top two, it shrank from 4.9% to 0.7%. **Open-weight vs. proprietary:** The gap between the best open-weight and proprietary frontier models shrank from 8% to 1.7% on some benchmarks in one year. The response from the research community has been harder benchmarks: ### GPQA Diamond Graduate-Level Google-Proof Q&A. Expert-curated science questions designed to resist web search. Still differentiating frontier models as of early 2026, though the gap is closing fast. Top scores now cluster above 94%. ### ARC-AGI-2 Abstraction and Reasoning Corpus, second generation. Tests novel pattern recognition that resists LLM-specific tricks. Gemini 3.1 Pro leads at 77.1%. Designed to be genuinely hard for current architectures, with an average model score of just 40.2%. ### Humanity's Last Exam Designed as the hardest available benchmark when frontier models approached saturation on everything else. Spans obscure graduate-level knowledge across scientific, mathematical, and humanities disciplines. Top models still score below 50%. ### SWE-bench Verified Fix real GitHub issues in open-source repos. This provides far stronger signal on practical coding ability than HumanEval or similar synthetic tests. OpenAI has [flagged contamination concerns](https://www.swebench.com/). ### Current Benchmark Scores (March 2026) #### GPQA Diamond: Graduate-Level Reasoning | Model | Score | |---|---| | GPT-5.4 (xhigh) | ~97.0% | | Claude Opus 4.6 | ~95.0% | | GPT-5.2 | ~95.0% | | Gemini 3.1 Pro | 94.3% | | Gemini 3 Pro | 91.9% | | o3 | 87.7% | | o4-mini | 78.4% | | Claude Sonnet 4.6 | 74.1% | #### ARC-AGI-2: Novel Reasoning | Model | Score | |---|---| | Gemini 3.1 Pro | 77.1% | | Claude Opus 4.6 | 68.8% | | GPT-5.2 Pro | 54.2% | | Gemini 3 Pro | 45.1% | #### SWE-bench Verified: Real-World Coding | Model | Score | |---|---| | Claude Opus 4.5 | 80.9% | | Claude Opus 4.6 | 80.8% | | Gemini 3.1 Pro | 80.6% | | GPT-5.2 | 80.0% | | Claude Sonnet 4.6 | 79.6% | **What the benchmark data tells you:** On GPQA Diamond, the top five models cluster within three points of each other. On ARC-AGI-2 (the hardest general reasoning benchmark), Gemini 3.1 Pro outperforms third place by 23 points. Real performance differences exist. They surface on genuinely hard tasks. The tasks that make up most GTM AI workflows (generating emails, summarizing calls, researching accounts, scoring leads) show small performance differences between frontier models. Cost, context window, latency, and ecosystem fit are more decisive factors. --- ## The Commoditization Thesis The [OpenRouter and Andreessen Horowitz joint study](https://arxiv.org/abs/2601.10088), published January 2026, analyzed metadata from over 100 trillion tokens processed through OpenRouter's routing platform. It is the largest empirical study of AI model usage patterns to date. The findings are the strongest evidence yet for the commoditization thesis. **Market structure is fragmenting.** Early in the study period, two DeepSeek models accounted for over 50% of all open-source token usage. By late 2025, no single model held more than roughly 25%. Leadership rotated between Qwen, Kimi K2, GPT-OSS variants, and others. **Open-weight models grew from a small fraction to roughly one-third of total usage** by late 2025. Closed proprietary models no longer dominate the volume picture. **Reasoning models now represent over 50% of all token usage** (up from near-zero in early 2024). Model architecture, not provider brand, is the primary driver of adoption patterns. The [Stanford AI Index 2025](https://aiindex.stanford.edu/report/) frames the same trend: "Performance gaps are shrinking." The Elo gap between top and 10th models halved in one year. The a16z conclusion is blunter: "The AI model itself is no longer a defensible competitive advantage. The application layer, proprietary data, and user experience are the durable moats." ### Where Model Choice Still Matters The commoditization thesis holds that within a given price tier, the differences between models are often smaller than the differences attributable to context quality. There are genuine exceptions. **On genuinely hard reasoning tasks.** A 23-point spread on ARC-AGI-2 between first and third place represents a real capability difference. For GTM workflows involving complex multi-source synthesis or edge case analysis, flagship models produce meaningfully better outputs. **On production reliability.** Benchmark scores and production reliability can diverge. Models with similar scores may behave differently on specific domain knowledge or prompt structures. Empirical evaluation on your actual workload is the only reliable test. **On task-specific profiles.** The OpenRouter data shows that different models dominate different use case categories. Anthropic Claude handles over 80% of programming tasks routed through the platform. DeepSeek was historically dominant in casual interaction. These usage patterns reflect real performance differences for those specific use cases. **On context window economics.** Google's Gemini 2.5 Flash offers a 1M context window at $0.30/$2.50 per million tokens. Claude Sonnet 4.6 offers the same 1M window at $3.00/$15.00. That is a 10x cost difference, entirely attributable to model choice. --- ## A Practical Model Selection Framework for GTM Given the convergence of frontier model capabilities, the most useful selection framework organizes around task characteristics rather than provider loyalty. ### Task-Based Routing ### High-volume commodity tasks: Use the cheapest capable model **Task examples:** Lead deduplication, basic field extraction from CRM notes, response intent classification (interested / not interested / needs follow-up), subject line generation, simple data formatting. **Why cheap works:** These tasks require instruction following and basic reasoning. The performance difference between GPT-4o mini and Claude Opus 4.6 on "extract the company name and title from this business card scan" is negligible. **Recommended models:** GPT-4o mini ($0.15/$0.60), Gemini 2.5 Flash-Lite ($0.10/$0.40), Claude Haiku 4.5 ($0.80/$4.00), Mistral Small 4. **Cost per task:** $0.001-0.005 at typical volumes. ### Mid-volume professional tasks: Use the mid-tier **Task examples:** Personalized outreach email generation, call summarization and CRM updates, account briefing for pipeline accounts, lead scoring with explanation, follow-up sequence generation. **Why mid-tier:** These tasks benefit from better instruction following, more reliable tone calibration, and stronger contextual reasoning than budget models provide. Mid-tier models hit the quality bar at 3-15x lower cost than flagships. **Recommended models:** Claude Sonnet 4.6 ($3.00/$15.00), GPT-4.1 ($2.00/$8.00), Gemini 2.5 Flash ($0.30/$2.50), o4-mini ($1.10/$4.40). **Cost per task:** $0.01-0.05 at typical volumes. **Note on Gemini 2.5 Flash:** For GTM workflows requiring larger context (10K-50K tokens), Gemini 2.5 Flash's 1M context window at $0.30/$2.50 pricing makes it the most cost-efficient option by a wide margin. ### Low-volume strategic tasks: Consider flagships when quality justifies cost **Task examples:** Strategic account briefs, multi-source synthesis (earnings + news + LinkedIn + CRM), pipeline analysis, ICP development from closed-won data. **Why flagships:** Output quality directly affects revenue decisions here. The frontier quality delta justifies the premium. A brief for a seven-figure account. A pipeline analysis informing quarterly forecast. Volume is low enough that absolute cost stays small. **Recommended models:** Claude Opus 4.6 ($5.00/$25.00), GPT-5.4 ($2.50/$15.00), Gemini 3.1 Pro ($2.00/$12.00). **Cost per task:** $0.05-0.50 at typical volumes. ### Reasoning-intensive tasks: Use reasoning models **Task examples:** Pipeline risk assessment, multi-step deal strategy, evaluating fit across competing criteria, objection pattern analysis. **Why reasoning models:** o3, o4-mini, and similar reasoning-first models use extended chain-of-thought that improves multi-step logic. For standard GTM tasks, the overhead adds cost without benefit. For genuinely complex analytical work, it matters. **Recommended models:** o3 ($2.00/$8.00), o4-mini ($1.10/$4.40), Claude Opus 4.6 (novel reasoning), Gemini 3.1 Pro (configurable depth). **Note:** o4-mini supports batch API pricing at $0.55/$2.20 with 24-hour latency. That is a 50% cost reduction for non-real-time analytics. ### The Open-Weight Option For organizations with engineering capacity and infrastructure, the Llama 4 models and Mistral open-source releases change the economics. Running models on your own hardware converts the cost from "per-token pricing" to "infrastructure amortization." Above a certain volume threshold, this reduces effective per-token costs by 5-10x. The practical threshold varies by organization. For most GTM teams running standard AI workflows, managed API services remain more cost-effective once engineering overhead is factored in. For platform builders or teams with very high-volume workloads (hundreds of millions of tokens per month), open-weight deployment deserves serious evaluation. --- ## The Real Competitive Advantage: Context Quality The most consistent finding across the research on AI model commoditization is that proprietary data and context quality are more durable competitive advantages than model choice. The a16z framing from the [100 trillion token study](https://arxiv.org/abs/2601.10088) is direct: proprietary data models are the foundation of durable AI moats, advantages that general AI models cannot replicate. A GTM AI system trained on your organization's specific deal outcomes, response rates, customer objection patterns, and ICP signals will outperform one running on the same frontier model with generic context. Research shows that 70-85% of AI project failures stem from data-related issues, with data quality as the primary culprit. Model selection problems account for a fraction of that figure. The "data moat" framing is evolving. The current version, as described in recent research, centers on **the live feedback loop**: each interaction generating proprietary signal (which emails got responses, which accounts converted, which objections killed deals) that compounds over time. That accumulation survives model switches. Competitors running the same frontier model have no way to access it. > **The practical implication** > > Before optimizing model selection, invest in context quality. Build the data infrastructure that captures your organization's specific deal intelligence. Structure that data for effective injection into AI workflows. A mid-tier model with excellent proprietary context will outperform a flagship model running on generic web data for your specific use cases. --- ## Putting It Together: A Decision Framework **Define the task category** Is this a high-volume commodity task, a mid-volume professional task, a low-volume strategic task, or a reasoning-intensive task? This determines the tier before you consider any specific model. **Identify your context window requirement** What is the maximum input context size for this workflow? If you need more than 128K tokens, your options narrow. If you need more than 200K tokens at budget pricing, Gemini 2.5 Flash or GPT-4.1 are the strongest choices. **Evaluate cost at your expected volume** Calculate cost at expected monthly volume across the models in your tier. At low volumes, cost differences are irrelevant. At high volumes, a 5x gap between GPT-4o mini and Claude Haiku 4.5 adds up fast. **Test on your actual workload** Benchmarks measure performance on standardized academic tasks. Your GTM workflow has specific characteristics: industry terminology, prompt structure, output format requirements, domain-specific knowledge. Run a structured evaluation on a representative sample before committing. **Build routing, not lock-in** Design your AI workflows to be model-agnostic at the architecture level. The frontier moves fast. The model that wins on your task profile today may lose in six months. Abstraction layers and routing logic let you swap models as the field evolves. --- ## The Bottom Line The model you choose matters, but within a constrained range. Benchmark convergence is real and accelerating. DeepSeek R1 achieving GPT-4-class performance at 27x lower cost was a signal. Open-weight models are within 1.7% of proprietary models on some benchmarks, down from 8% a year earlier. For most GTM use cases, the performance gap between frontier models from OpenAI, Anthropic, and Google is smaller than the gap attributable to context quality, prompt design, and workflow architecture. Where model choice genuinely matters: tasks requiring novel reasoning at the frontier, workflows with specific context window requirements, and cost optimization at scale. Where the real advantage sits: what context you provide, how you structure it, what proprietary data you bring, and whether your workflows capture the deal intelligence that compounds over time. --- *Research sources: ["State of AI: An Empirical 100 Trillion Token Study with OpenRouter"](https://arxiv.org/abs/2601.10088), OpenRouter + a16z, January 2026. [Stanford HAI AI Index 2025](https://aiindex.stanford.edu/report/). ["When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation"](https://arxiv.org/abs/2602.16763), February 2026. Benchmark scores from [Artificial Analysis](https://artificialanalysis.ai), [llm-stats.com](https://llm-stats.com), [Epoch AI](https://epoch.ai/benchmarks), and model-specific documentation as of March 2026. Pricing data from [pricepertoken.com](https://pricepertoken.com) and official provider pricing pages, March 2026.* ================================================================================ # The GTM Laws of Physics > A hierarchy that determines why some AI-powered GTM teams produce extraordinary results while others generate expensive noise. Context > Timing > Targeting > Content. **Date:** 2026-03-01 **Source:** https://gtm.ai/guides/the-gtm-laws-of-physics --- # The GTM Laws of Physics **Context > Timing > Targeting > Content** Building the machine-readable, AI-ready GTM data foundation from grounding data to context graphs. ## Executive Summary Every GTM team is racing to embed AI into their revenue motions. The overwhelming majority of AI initiatives stall before they produce measurable outcomes. The root cause is rarely the AI model itself. The models are now a commodity and alone do not provide a competitive advantage. The data beneath the model is what gives any one company a proprietary moat. This guide introduces a governing principle we call the GTM Laws of Physics: a hierarchy that determines why some AI-powered GTM teams produce extraordinary results while others generate expensive noise. **Context > Timing > Targeting > Content** These laws operate like actual physics. You cannot violate them and expect good outcomes. You cannot time your way out of bad context. You cannot target your way out of missed timing. Great content will never compensate for sloppy targeting. Each law depends on the one above it. The returns compound in order. Context is the First Law because context is the AI data foundation. An AI model is only as intelligent as the structured context feeding it. To operationalize that context, we introduce the Four Foundational Layers: a build-from-the-bottom-up architecture of Grounding Data, Unification, Context Graph, and Surface Areas that turns raw first-party and third-party data into an AI-ready GTM intelligence layer. We illustrate this through three customer deployments: Asana, Vanta, and Ramp. Each used this framework as the backbone to build all four layers and deliver AI-powered GTM outcomes that respect the Laws of Physics. ## The Four Laws In physics, fundamental laws govern what is possible. Gravity does not care about your intentions. GTM has its own set of governing laws, and AI has made them more visible. The reason most AI implementations underperform is that organizations try to use AI to violate the laws. Companies deploy sophisticated content generation on top of poor targeting, or deliver messaging when the prospect bought a competitor last week. The laws are sequential and hierarchical. | Law | Order | What It Means | |---|---|---| | **Context** | 1st | Without rich, structured context, every downstream GTM motion is flying blind. Context is the data foundation. | | **Timing** | 2nd | You cannot time your way out of bad context. With the right context, you reach accounts at the moment they are ready to engage. | | **Targeting** | 3rd | Precise targeting depends on context (who to reach) and timing (when to reach them). No segmentation compensates for bad fit or a recent competitor win. | | **Content** | 4th | Personalized content is the final mile. Great content cannot fix sloppy targeting. Content is only as good as the data powering it. | ## First Law: Context Context is the foundational law because it represents everything you know about an account, a buyer, and the market they operate in. Context includes firmographic data (who they are), technographic data (what they use), conversation intelligence (what they have said), and product usage data (how they have engaged). It also includes corporate hierarchy (how they are structured), news and scoops (what is changing), and intent signals (what they are researching). When context is rich, structured, and machine-readable, AI can reason about accounts the way your best rep does, synthesizing dozens of signals into a coherent view. When context is thin or fragmented, AI produces generic output regardless of how sophisticated the model is. An AI that lacks firmographic data cannot score an account. An AI that lacks conversation history cannot personalize a follow-up. An AI that lacks hierarchy data cannot map a buying committee. Context is the physics that makes everything else possible. Without it, every downstream motion (timing, targeting, content) degrades. ## Second Law: Timing With context in place, timing becomes the next lever: the ability to reach an account at the moment they are most likely to engage. Triggers include intent signals, funding events, leadership changes, technology evaluations, and contract renewal windows. Timing-based signals compound when stacked on top of each other. Timing without context is noise. An intent signal that says "Company X is researching project management software" is meaningless if you do not know Company X's industry, tech stack, buying committee, conversation history, and fit for your product. You cannot time your way out of bad context. ## Third Law: Targeting Targeting is the selection of which accounts and which personas to pursue. It depends on context to define fit, timing to prioritize urgency, and qualification to determine whether you should sell to them at all. The best ICP models combine firmographic fit, technographic alignment, intent signals, and engagement history into a composite score. Fit comes first. Then propensity: are they in-market now, or about to be? Targeting cannot fix what timing and context get wrong. A perfectly segmented list will not respond if they bought your competitor last week. ## Fourth Law: Content Content is the final mile: the email, the talk track, the deck, the advertisement, and the demo. AI has made content generation faster and cheaper than ever. Content is also the most dependent law: it inherits the quality of every law above it. A personalized email powered by deep account context, perfect timing, and precise targeting feels like it was written by a human who did their homework. The same template sent to a poorly targeted list with no contextual data feels like spam. The laws are sequential, and the returns compound in order. ## The Four Foundational Layers The Laws of Physics tell you why context is the highest-order priority. The Four Foundational Layers tell you how to build it. AI-powered GTM is a foundation you build. Four layers, each unlocking new capability. You cannot skip stages: each layer depends on the one below it. | Layer | Name | What It Provides | |---|---|---| | **Layer 4** | Surface Area | Skills, agents, and automated workflows. The location where AI jobs are actually executed, running on verified, unified, connected data. This should be done in as few surface areas as possible (Salesforce, ZoomInfo, Claude, etc.). | | **Layer 3** | Context Graph | Connected entities, signals, and causal chains. Databases store records; context graphs store meaning. The relationship between a contact and a company has a start date, seniority level, and influence score. | | **Layer 2** | Unification | Entity resolution: first-party and third-party data as one. "Acme Corp" in your CRM and "ACME Corporation" in billing resolved into a single canonical entity. AI queries one universe. | | **Layer 1** | Grounding Data | Verified B2B world model: companies, contacts, and signals. Confidence-scored, attribute-level verified, and continuously refreshed. Your CRM is a log of manual input. Grounding data is the world model. Start here. | ### Layer 1: Grounding Data Your CRM is not a world model as it stands today. It is a record of what your team has logged, and that record has gaps. Contacts who never got entered. Companies named inconsistently. Job titles that have not been updated in two years. Signals that happened and were never captured. Before AI can reason about your market, it needs a verified world model of B2B reality. This is grounding data: the comprehensive, continuously refreshed foundation of who companies are, who works there, what they are doing, and what signals they are showing. Good grounding data is confidence-scored, attribute-level verified, and continuously refreshed. B2B data decays fast. The VP of Sales you called last quarter may have changed companies. The startup that was 50 people is now 200. Stale grounding data means confident wrong answers from AI. **Without grounding data:** AI searches the web and returns outdated info. Contact details are wrong or missing. Company context is generic and shallow. Signals and changes stay invisible. **With grounding data:** Verified data on your entire TAM and buying committee. Real-time signals surfacing hiring, funding, and tech changes. Intent data showing who is actively researching solutions like yours. Confidence scoring so AI knows the reliability of every data point. The difference is structural. ### Layer 2: Unification You now have grounding data: a verified world model of B2B reality. You also have first-party data: your CRM records, call transcripts, email history, deal outcomes, product usage, ICP definitions. These two data sets describe the same universe. They just do not know it yet. "Acme Corp" in your CRM. "ACME Corporation" in billing. "Acme Co." in your email tool. "Acme" in Slack. These are the same company. Until you resolve them into a single canonical entity, an AI querying your systems gets four partial pictures instead of one complete view. Unification means entity resolution at scale: matching, deduplicating, and linking records across every system until you have a single universe. This is what makes your data machine-legible. The machine cannot intuit that four spellings mean one company. You have to tell it. - **Entity Resolution:** Matching billions of records across every variation, misspelling, and format. Knowing "Cisco Systems Inc." and "CSCO" and "Cisco (WebEx division)" are the same entity graph. - **Semantic Normalization:** "VP Sales" = "Vice President of Sales" = "Head of Sales" = same buying committee role. GTM data must be machine-readable across systems. - **Data Warehouse Integration:** A centralized hub (Snowflake, Databricks) consolidating CRM, conversation intelligence, grounding data, and enrichment feeds into one queryable layer. There is an old story about three blind men and an elephant. The first grabs the trunk and declares it a snake. The second presses his palm against the side and insists it is a wall. The third wraps his hand around the tail and argues it is a rope. Each is confident. Each is wrong. They do not lack intelligence. They lack context. This is precisely what happens inside most GTM organizations today. The AE just added Coca-Cola to their pipeline as a greenfield opportunity. The SDR is three touches deep into a cold sequence targeting the VP of IT. The Account Manager who owns the relationship just got off a call and learned they signed with a competitor two weeks ago. Three people, one account, three completely different pictures of reality. The AI tools sitting on top of their fragmented data are just as blind. No model fixes this. No sequence fixes this. No content fixes this. The only fix is a complete, unified picture of the account before anyone touches it. That is what the First Law demands. ### Layer 3: The Context Graph Unified data is cleaner data. It is still just data: rows and columns, records and attributes. The context graph transforms unified data into something an AI can actually reason over. A context graph connects entities by their relationships, events, and patterns. Query "Acme Corp" and you get a full picture: the org chart, your complete conversation history, open headcount and recent funding, and the VP of Sales who just moved companies. The context graph gives you what the winning move looks like for deals at this stage across companies of the same size, in the same industry, with the same buying committee engaged. One query. The context graph also preserves causality. A CRM shows you that a deal moved to "Proposal" and then the close date pushed three months. A context graph shows you the why: a CFO joined discovery and asked detailed ROI questions, moving the deal forward; the champion flagged needing unplanned executive approval, pushing the close. Similar deals with this pattern push an average of two months. Now you know what to do next. Databases store records. Context graphs store meaning. The relationship between a contact and a company has a start date, a seniority level, and an influence score. That is what AI needs to reason well. AI reasoning over a CRM generates generic advice. AI reasoning over a context graph generates specific, actionable, accurate guidance. ### Layer 4: Surface Area Once the foundation is right, you build AI operations on top: skills, agents, and automated workflows running on verified, unified, connected data. This is where AI actually executes. **Automated Account Planning.** AI synthesizes the context graph (firmographics, call transcripts, deal history, news signals) to produce comprehensive account briefs. Pure First Law work. **Signal-Driven Prospecting.** AI monitors intent signals, funding events, and technology adoptions to surface in-market accounts. **Pipeline Forecasting.** AI analyzes conversation sentiment, engagement velocity, and historical patterns from the context graph to produce probabilistic forecasts. **Lead Scoring and Routing.** AI combines fit data with behavioral data to score and route leads in real time. **Personalized Outbound Generation.** AI drafts emails and talk tracks using account-specific context from the graph. Content that only works because the three laws above it are in place. Operations happen within a chosen surface area: CRM-native (Salesforce, HubSpot), AI assistants (Claude or Copilot via MCP architectures), sales engagement platforms, or custom interfaces. The choice depends on how your team works. Regardless of surface area, the operations layer only performs as well as the foundational layers beneath it. **The maturity principle:** Your foundation determines your ceiling. Clean grounding data gives you basic context for account briefs. Add unification and you can reason across systems. Build a context graph and you access causality, deal patterns, and real intelligence. Reach full operations and your AI runs on verified, connected, meaningful data, producing guidance that feels like it came from your best rep. ## The Laws in Practice The following examples each apply the Laws of Physics and build the Four Foundational Layers. Each takes a different architectural path, but all respect the same sequence: grounding data first, then unification, then context graph, then operations. Context before timing. Timing before targeting. Targeting before content. ### Cross-Sell and Expansion at an Enterprise SaaS Company **Use Case:** Cross-sell and expansion **Job:** Account prioritization and personalized outbound **Surface:** Salesforce with a custom AI layer **Data:** Data warehouse, B2B data provider, conversation intelligence, CRM A large enterprise SaaS company with over 1,800 employees and a growing enterprise segment needed AI to help their SDRs, AMs, and AEs focus on the right accounts at the right time. The problem was a lack of structured, unified context. They had data everywhere, but the Four Foundational Layers were not in place. **Grounding Data:** A B2B data provider serves as the verified world model, providing firmographic, technographic, and news data that internal systems cannot generate. With 61,000 whitespace accounts processed for enrichment, grounding data provides the baseline context that makes every downstream motion possible. **Unification:** The team migrated their data warehouse to enable bulk processing of call transcripts with speaker-level detail. A unified analytical layer now resolves conversation transcripts, CRM activity, and firmographics into a single view. One person owns it all. A dedicated enrichment product owner manages consolidation across providers. **Context Graph:** The differentiator is how the system connects entities, events, and meaning. The AI layer does not just know that a company has 500 employees. It knows their VP of Engineering mentioned a competitor on a call last Tuesday, that the company just raised a Series C, and that CRM data shows three open opportunities across business units. One query surfaces all of this. The context graph connects these data points into a causal narrative AI can reason over. **Surface Area:** The AI layer (embedded in the CRM account page) generates personalized emails that reference real buyer language from call transcripts and real company context from the grounding data. The system prioritizes accounts based on multi-signal context. It is now expanding beyond sales into HR and legal use cases via MCP server architecture, proving that a well-built context layer becomes a platform. **Laws of Physics:** Context (grounding data, conversation intelligence, CRM) then Timing (news triggers and intent signals) then Targeting (whitespace scoring across 61K accounts) then Content (AI-personalized outreach from real buyer language). Every law respected in order. ### Consolidating a Fragmented Data Architecture at a Trust Platform **Use Case:** New logo acquisition at scale **Job:** Lead enrichment and intent targeting **Surface:** Dual CRM (Salesforce and HubSpot) **Data:** Orchestration layer, data warehouse, B2B data share A fast-growing trust management platform with over 14,000 customers was scaling its SDR, AE, and AM teams at speed. That velocity exposed a fundamental problem: data fragmented across ten or more enrichment vendors. No grounding data layer. No entity resolution. No context graph. Operations were running on top of an incomplete, conflicting foundation: a direct violation of the Laws of Physics. **Grounding Data:** A strategic multi-year agreement established a verified B2B world model as the single source of truth. A canonical company identifier became the key that enables unification across every system. **Unification:** A three-tier architecture replaced the fragmented vendor stack. First, an orchestration layer handles scheduled bulk enrichment and real-time triggered updates, matching against canonical IDs. Second, a data warehouse consolidation hub receives 800,000 matched accounts and 1.8 million contacts, with deduplication as the primary objective. Third, enriched data flows into both CRMs via automated routing. **Context Graph:** With a unified identity layer in place, the team activated intent and signal data as custom objects in Salesforce, connecting grounding data (who companies are) with signal data (what they are doing right now). Audience creation from pre-built data cubes allows the team to query the full context graph rather than static CRM reports. **Surface Area:** SDRs now operate with consistent, enriched account context regardless of which CRM they work in. Intent signals power upmarket segment targeting. Enrichment economics dropped to approximately four cents per record. AI-driven audience segmentation became possible for the first time. **Laws of Physics:** Context (consolidated identity layer) then Timing (intent and signal triggers) then Targeting (enriched audience segmentation at scale) then Content (consistent account context for SDR outreach). The sequence that was impossible when five vendors created five conflicting pictures of reality. ### Building a Custom GTM Engine at a High-Growth Fintech **Use Case:** Vertical market expansion **Job:** Signal-driven targeting and waterfall enrichment **Surface:** Custom internal GTM platform **Data:** Full B2B data cube, data warehouse, waterfall API A high-growth corporate finance platform took the most ambitious approach. Rather than operating AI within an off-the-shelf CRM, the team purchased a full B2B data cube and built a hybrid internal GTM engine. Grounding data is treated as core infrastructure. **Grounding Data:** The full data cube sits in a data warehouse as the verified B2B world model. Rather than making API calls for individual records, the team has the complete dataset, enabling custom scoring models, vertical-specific targeting logic, and proprietary enrichment workflows that would be impossible with seat-based SaaS tools. **Unification:** A waterfall enrichment model ensures completeness: the data cube serves as the primary source, followed by API-based real-time lookups, with additional providers as fallback. The data team combines firmographics with proprietary signals: franchise hierarchical IDs (mapping multi-unit operators to holding companies), early-stage startup formation data, and spend pattern intelligence from their own financial platform. **Context Graph:** The context graph runs deep in vertical markets. For PE/VC firms, it maps fund structures to portfolio companies to operating partners across over 100,000 contacts. Franchises: multi-unit operators resolved to holding companies at a 96% match rate. Accounting firms: hundreds of thousands of contacts across practice areas. AI reasons over every edge. **Surface Area:** The team expanded their targetable market to over 40 million US records in the sub-10 employee segment. Contact-first outbound became account-based, signal-driven outreach, with intent data identifying accounts showing buying signals. Next: MCP server integration for real-time AI access. **Laws of Physics:** Context (full data cube and proprietary signals) then Timing (multi-topic intent triggers) then Targeting (vertical-specific scoring across PE/VC, franchises, accounting) then Content (account-based, signal-informed outreach). The most complete expression of all four laws and all four foundational layers. ## Models Are Commodities. Context Is the Moat. Every company has access to the same models, available to anyone at commodity prices. Two teams running identical models will produce wildly different outputs, and the difference comes entirely from what they feed those models. The team that builds a superior context layer (unified data, resolved identities, connected signals) will consistently outperform. This contextual layer, a combination of first-party and third-party data, provides companies with a proprietary data foundation that their competition does not have. The implication: AI strategy is data strategy. The variable that matters is what your AI knows about your market, your accounts, and your GTM motion, and how you keep that knowledge current. The model is interchangeable. The context layer is not. This is why the Laws of Physics hold. The model you choose sits at the Surface Area layer. It runs on top of your context graph, your unified identity layer, and your grounding data. Swap one model for another and the outputs shift. Remove the context layer and the outputs collapse. The compounding effect: organizations that invest in context see returns that accelerate over time. Every deal outcome, every conversation transcript, every enrichment cycle adds signal to the context graph. The AI gets smarter because the data improves, regardless of the model. Companies that start building this foundation today create a compounding advantage that late movers cannot replicate by purchasing a better model. ## Conclusion: Respecting the Laws, Building the Layers The three examples share a common pattern. None started by selecting an AI model. None started by generating content. None started by building targeting lists. They all started by building context, the First Law, from the ground up through the Four Foundational Layers. 1. **Start with grounding data.** Your CRM is not a world model. Before AI can reason about your market, it needs a verified, continuously refreshed foundation. 2. **Unify relentlessly.** Entity resolution is not a one-time project. It is the ongoing work of making sure every system sees the same canonical truth. One team unified in a data warehouse. Another used an orchestration layer. A third went with a full data cube and waterfall. Different methods, same principle: one entity, one truth. 3. **Build the context graph.** Databases store records. Context graphs store meaning. The organizations that built causal, relationship-aware data layers got AI that produces specific, actionable guidance. Those that stopped at unified tables got better reports. They did not get intelligence. 4. **Run operations on the foundation.** AI jobs (account planning, signal-driven prospecting, personalized outbound) only work when the layers beneath them are solid. Content is the final mile. Targeting is powerful only when it operates on rich context. The organizations that will lead the AI-powered GTM era are the ones that respect the Laws of Physics: Context > Timing > Targeting > Content. Build grounding data. Unify your systems. Construct a context graph. Then, and only then, run agentic workflows on top. ================================================================================ # Tokens and Context: The GTM AI Mental Model > How to think about token usage, context windows, and context quality for go-to-market AI. Includes research-backed guidance on context rot, cost economics, and how to architect AI workflows that actually work in production. **Date:** 2026-03-20 **Source:** https://gtm.ai/guides/tokens-and-context-for-gtm-ai --- Every GTM AI workflow runs on tokens. Every dollar you spend on AI is a dollar spent on tokens. And whether your AI agent produces useful output or expensive hallucination comes down, more than almost anything else, to what tokens you put in the context window and how you put them there. This guide builds the mental model: the working intuition that lets you make good architectural decisions, avoid the most expensive mistakes, and understand why some AI outputs are sharp and others are garbage. Think of it as the reasoning layer beneath the API documentation. ## What a Token Actually Is A token is the fundamental unit of text that language models process. It is not a word and it is not a character. It sits somewhere in between. **The practical conversion:** - 1 token = approximately 4 characters of English text - 1 token = approximately 0.75 words - 750 words = approximately 1,000 tokens - To estimate tokens from a word count: multiply by 1.3 to 1.4 For common words, one word is often one token. For less common words, longer words, and technical terminology, a single word might become two or three tokens. Code and structured data tokenize less efficiently still. Non-English text is often worse. **Why it matters:** Every model has a context window measured in tokens. Every API call is priced per token. Once you internalize these conversion rates, you can estimate the size and cost of any workflow before you build it. ## Token Counts for Common GTM Documents Before you can reason about context, you need a feel for how large typical GTM documents actually are in token terms. | Document type | Approximate token count | |---|---| | Outbound sales email (150-250 words) | 200-325 tokens | | Well-personalized email with research hook | 300-500 tokens | | 3-step cold email sequence (all three emails) | 600-1,000 tokens | | LinkedIn profile (full) | 600-1,200 tokens | | CRM contact record (basic fields + notes) | 300-500 tokens | | CRM contact record (full, with activity history) | 1,000-3,000 tokens | | 30-minute sales call transcript | 5,600-6,000 tokens | | 60-minute discovery call transcript | 10,000-12,000 tokens | | Light account brief (name, industry, size, key products) | 400-600 tokens | | Full account brief (news, tech stack, contacts, open deals) | 2,000-6,000 tokens | | Deep research document (analyst-grade) | 8,000-20,000 tokens | | 10-K filing or annual report | 50,000-150,000 tokens | A useful calibration: a complete, well-researched account brief for a single prospect sits around 4,000-6,000 tokens. A single AI SDR workflow that researches a prospect and generates a personalized email sequence consumes roughly 3,000-5,000 tokens per prospect, all in. ## Context Windows: What the Numbers Mean Every model comes with an advertised context window: the maximum number of tokens it can process in a single call. These numbers have grown dramatically. Models that topped out at 4,096 tokens in 2022 now commonly offer 128K, 200K, or 1 million tokens. ### 128K tokens Approximately 96,000 words. Enough to hold the full text of a novel, or roughly 20 detailed account briefs simultaneously. ### 200K tokens Approximately 150,000 words. Roughly 30 full account briefs, or a complete mid-size sales pipeline with full deal history. ### 1M tokens Approximately 750,000 words. An entire company CRM for a small team, or a year of sales call transcripts. ### 10M tokens Meta's Llama 4 Scout context window. The entire works of Shakespeare fit in roughly 900,000 words, leaving room for 9 million more. Large context windows are genuinely useful. But the number the model advertises and the context the model actually uses effectively are two different things. ## The Gap Between Advertised and Effective Context This is where most GTM AI builders go wrong. ### Lost in the Middle In 2023, a Stanford research team published ["Lost in the Middle: How Language Models Use Long Contexts"](https://arxiv.org/abs/2307.03172) in the Transactions of the Association for Computational Linguistics. The finding was counterintuitive and has since become one of the most referenced results in applied AI. > Models exhibit a U-shaped performance curve across their context window. Performance is highest when relevant information appears at the beginning or end of the context. Performance degrades significantly when relevant information is buried in the middle. **Performance can degrade by more than 30%** when the same fact moves from the start or end of a context window to the middle. This holds even for models explicitly designed for long-context tasks. What this means in practice: load an account brief, three past call transcripts, a competitor analysis, and a list of product features into a large context window. If the specific fact that should drive your email hook is buried in transcript number two, the model is substantially more likely to miss it, misremember it, or ignore it entirely. The most critical information belongs at the top of your context, or at the very end. Always the edges. ### Context Rot In July 2025, Chroma published ["Context Rot: How Increasing Input Tokens Impacts LLM Performance,"](https://research.trychroma.com/context-rot) a study covering 18 state-of-the-art models including GPT-4.1, Claude 4, Gemini 2.5, and Qwen3. The central finding: **every single model tested degraded in performance as input context length increased**, even on simple tasks like retrieval and text replication. > **Context Rot vs. Context Overflow** > > Context overflow is hitting the token limit and getting an API error. Context rot is different: it is the silent degradation that sets in well before the limit, as the model's attention spreads thinner with every additional token you add to the prompt. A model with a 200K token context window can show significant accuracy loss at 50K tokens. Overflow throws an error. Rot degrades your outputs without warning. The mechanism is attention. Every token competes with every other token for the model's focus. As context grows, each token gets proportionally less of it. The model's working memory is finite. Adding tokens beyond what the task needs dilutes the signal. In practice, most models effectively utilize only 10-20% of their theoretical context window for complex tasks. Models in the Chroma study "fell far short of their Maximum Context Window by as much as >99%" in real task performance. ## Context Stuffing: The Anti-Pattern With million-token context windows now available and prices falling, there is a tempting architectural pattern: dump everything into the context and let the model sort it out. Every CRM record, every call transcript, every piece of company research, every product document. All of it, in one prompt. This is called context stuffing. Production AI research identifies it as the single largest source of wasted compute, hallucinated outputs, and unreliable agent behavior in GTM pipelines. **Why context stuffing fails:** 1. **The Lost in the Middle effect.** Critical information in the interior of a large context gets missed or misrepresented. The problem scales with context size. 2. **Context rot.** Performance degrades as tokens accumulate, even before you approach the limit. 3. **Attention dilution.** The model's attention budget is finite. More noise means less focus on the signal that actually matters. 4. **Hallucination amplification.** More context means more potentially conflicting information. Conflicts and distractors increase hallucination rates. 5. **Cost and latency.** A 200K token prompt for a task that actually needed 8K tokens costs 25 times more and takes substantially longer. In production, these effects compound. Research comparing context stuffing directly to retrieval-augmented generation (RAG) found that RAG achieved equivalent output quality with less than half the tokens and roughly half the latency. ## Context Quality vs. Context Quantity The most important insight in applied AI for GTM is this: **the quality of your context matters more than the size of it**. A tightly selected 5,000-token context with the right account signals, the relevant call transcript, and a clear ICP definition will outperform a bloated one. Even 50,000 tokens performs worse if 40,000 of them are loosely related noise. This has direct implications for how you build GTM AI workflows: **Be selective about what goes in** For any given task (outreach generation, account briefing, lead scoring), define the minimum viable context set. What are the three to five pieces of information that most directly determine output quality? Put those in. Not everything that might be relevant. **Put critical information first** The Lost in the Middle research is consistent: primacy bias is real. The most important signals belong at the top of the context. If there is one piece of information that should drive the AI's output, it belongs in the first few hundred tokens. **Use retrieval rather than bulk loading** For workflows that draw on large knowledge bases (product documents, competitive intel, CRM history), retrieve the most relevant chunks rather than loading everything. Semantic search or structured filtering lets you feed a 4,000-token context with exactly the right material instead of a 100,000-token context with the right material diluted by 90,000 tokens of less relevant content. **Summarize before injecting** Long documents benefit from a summarization step before injection. A 12,000-token call transcript summarized down to a 1,000-token structured brief before being used as context for outreach generation produces better outreach than the raw transcript injected in full. **Use agent chains for complex tasks** Google Research's ["Chain of Agents"](https://arxiv.org/abs/2406.02818) work shows that breaking complex, document-heavy tasks across multiple agent calls outperforms feeding one massive context to a single model. Instead of one call with 50 documents in context, five sequential calls each focusing on a subset of the material, building toward a final synthesis, produces sharper outputs with less context rot. ## RAG vs. Long Context: What the Research Says A January 2025 paper directly comparing long-context LLMs against retrieval-augmented generation, ["Long Context vs. RAG for LLMs"](https://arxiv.org/abs/2501.01880), produced findings worth understanding before you architect GTM AI pipelines. ### Long context wins when... The task genuinely requires synthesizing information across an entire long document: summarizing a lengthy contract, analyzing a full earnings call, reviewing a comprehensive competitive report. When breadth of coverage matters and the document is the unit of analysis, feeding the whole thing in context produces better results. ### RAG wins when... You are retrieving specific facts or signals from a large corpus: finding the right contact in a database, pulling the relevant section of a playbook, looking up previous deal history. When precision matters and the relevant information is a small fraction of the total available, RAG is both more accurate and significantly cheaper. For most day-to-day GTM AI use cases (outreach generation, lead scoring, call summaries, account briefs), RAG or selective context injection outperforms bulk context loading. The exception is synthesis-heavy tasks (full document analysis, contract review, earnings call synthesis) where the model needs to reason across the entire scope. That's a narrow category. ## Token Economics: What Things Actually Cost The cost of AI has collapsed. As of March 2026, the price per token has declined roughly 280x from GPT-3.5 launch in November 2022. A workload that cost $10,000 per month in model API fees in 2023 likely costs $50-200 today. Price ranges across models are wide. Routing decisions matter: #### Per-call cost: 10,000 input tokens, 1,000 output tokens | Model | Total cost | |---|---| | GPT-4o mini | $0.0021 | | Gemini 2.5 Flash | $0.0055 | | o4-mini | $0.0154 | | Gemini 2.5 Pro | $0.020 | | GPT-4.1 | $0.028 | | Claude Sonnet 4.6 | $0.045 | | Claude Opus 4.6 | $0.075 | #### Per-call cost: 100,000 input tokens, 5,000 output tokens | Model | Total cost | |---|---| | GPT-4o mini | $0.018 | | Gemini 2.5 Flash | $0.043 | | Gemini 2.5 Pro | $0.300 | | GPT-4.1 | $0.240 | | Claude Sonnet 4.6 | $0.375 | | Claude Opus 4.6 | $0.625 | | GPT-5.4 | $0.325 | At 100K input tokens, the spread between the cheapest and most expensive options is more than 30x. **Context architecture is cost architecture.** This is another reason to avoid context stuffing. Every token you trim from the context window is a direct cost reduction. Output quality often improves at the same time, because of context rot. ## A Framework for GTM Token Budgets Not every task needs the same context depth. A practical mental model: ### High-volume, low-complexity tasks (100K+ per month) **Examples:** Lead deduplication, basic CRM field extraction, response classification, subject line generation. **Token budget per task:** 500-2,000 tokens input, 100-300 tokens output. **Recommended approach:** Lean, structured prompts. Include only the exact fields needed. No research context. **Cost target:** Under $0.005 per task. At GPT-4o mini pricing, this is roughly $0.001-0.003 per task. ### Mid-volume, moderate-complexity tasks (10K-100K per month) **Examples:** Personalized outreach generation, call summaries, account briefing for known accounts, lead scoring with explanations. **Token budget per task:** 2,000-8,000 tokens input, 300-1,500 tokens output. **Recommended approach:** Selective context injection. Pull the three to five most relevant signals for the specific task. Summarize long documents before injecting. **Cost target:** $0.01-0.05 per task. Mid-tier models (Sonnet 4.6, GPT-4.1, Gemini 2.5 Flash) are the right tier. ### Low-volume, high-complexity tasks (under 10K per month) Full strategic account briefs for enterprise targets, multi-source synthesis, pipeline analysis, deal coaching from complex deal histories. Token budget: 8,000-50,000 tokens input, 1,000-5,000 tokens output. Approach: Use the full available context, but structure it carefully. Put synthesis requirements first, supporting documents after. Use the flagship tier when accuracy justifies the cost. Cost: $0.05-0.50 per task. Flagship models (Opus 4.6, GPT-5.4, Gemini 3.1 Pro) are the right tier here. ## What This Means for GTM AI Pipelines The research points toward a consistent architectural principle: **structured, targeted context outperforms bulk context at every price point**. The GTM AI workflows that perform best in production thought carefully about what the model needs for each task. They retrieve exactly that and structure it with the most important signals first. The practical version of this principle looks like: - An AI SDR tool that pulls a prospect's recent job change, one relevant news item, and the ICP definition for that segment beats one that dumps the full LinkedIn history, every news article from the past year, and all prior email history into a single prompt. - A call summary workflow that feeds the transcript plus the CRM schema beats one that also injects the company profile, the contact's LinkedIn, and the rep's full account history. - A lead scoring system that retrieves the ten most predictive firmographic signals plus one or two behavioral indicators beats one that loads every available data field. The model is capable. The question is whether you are giving it the right raw material to work with. Fewer, better-selected tokens consistently outperform more tokens of loosely relevant context. > **The single most important takeaway** > > Quality of context is the primary driver of AI output quality in GTM applications. Model choice is secondary. Context architecture is where the work is. --- *Research sources: Liu et al. (2023), ["Lost in the Middle: How Language Models Use Long Contexts,"](https://arxiv.org/abs/2307.03172) Stanford / TACL. Chroma Research (July 2025), ["Context Rot: How Increasing Input Tokens Impacts LLM Performance."](https://research.trychroma.com/context-rot) ["Long Context vs. RAG for LLMs: An Evaluation and Revisits"](https://arxiv.org/abs/2501.01880) (January 2025). Google Research, ["Chain of Agents: Large Language Models Collaborating on Long-Context Tasks"](https://arxiv.org/abs/2406.02818) (NeurIPS 2024). Token pricing data from [pricepertoken.com](https://pricepertoken.com) and official provider pricing pages, March 2026. Token economics via [Stanford AI Index 2025](https://aiindex.stanford.edu/report/).* ================================================================================ # Blog ================================================================================ # Introducing the ZoomInfo MCP > Revenue teams are adopting AI fast, but foundation models lack the go-to-market context sellers actually need. The ZoomInfo MCP server connects your AI tools to the B2B data layer that makes their output useful. **Date:** 2026-02-24 **Source:** https://gtm.ai/blog/introducing-zoominfo-mcp --- Revenue teams are adopting AI faster than almost any other function in the enterprise. They're using it for account research, call preparation, prospecting, competitive analysis, and outreach. The tools are impressive. The underlying models are powerful. And yet, the output these tools produce for go-to-market work is, for the most part, not useful enough to change how a frontline seller actually operates. The reason is straightforward: the AI is missing the data that go-to-market execution depends on. ## The data gap in go-to-market AI Every major AI vendor in go-to-market is building on the same foundation models from OpenAI, Anthropic, and Google. The models themselves are largely commoditized. The differentiation between AI tools has very little to do with the underlying intelligence and almost everything to do with the data those tools can access. Foundation models are trained on the public internet. They understand business concepts in the abstract. They can structure an email, draft discovery questions, and summarize a company's public profile. What they cannot do is tell you that Acme Corp posted 12 SDR roles in the last 30 days, that your main contact was promoted to VP of Revenue Operations three months ago and used a competitor's product at her last company, that similar accounts in your pipeline close at 34% when you lead with operational efficiency messaging, or that on last Tuesday's call the prospect pushed back on implementation timeline. That kind of context, the kind that actually determines whether a seller walks into a meeting prepared or unprepared, doesn't exist on the public internet. Most of it doesn't exist in any single system. It's spread across CRMs, conversation intelligence platforms, email threads, intent data providers, and, often, the seller's own memory of interactions they never logged. Without access to this context, AI tools produce output that is structurally generic. The responses are well-formed and reasonable-sounding, but they contain nothing specific enough to change a seller's behavior. A call prep that doesn't reference the last conversation, the account's current signals, or the competitive dynamics at play is a call prep that the seller will ignore, and rightly so. ## Why this problem is harder for AI than it was for humans For two decades, the challenge in go-to-market has been getting the right information to the right seller at the right time. ZoomInfo has been in this business for nearly all of that period, building the foundational B2B data layer, 100 million companies, 600 million contacts, signals, intent data, technographics, and org charts, that the best revenue teams in the world use to operate. In the pre-AI world, that data enabled sellers directly. It powered list building, territory analysis, CRM hygiene, and lead routing. Sellers used it to find the right people, understand what was happening at target accounts, and decide where to focus. In the AI world, the same data layer is just as critical, but the requirements are more demanding. Sellers have always been remarkably good at compensating for imperfect information. They ask colleagues on Slack, they draw on conversations they remember but never logged, they read between the lines of a LinkedIn post. They operate within a network of relationships and informal knowledge that helps them fill gaps on the fly. AI systems have none of these compensating mechanisms. When an AI tool encounters a gap in its available data, it doesn't flag the gap or go seek additional context. It generates a plausible-sounding response based on whatever information it does have. The result is output that sounds confident but is often disconnected from the reality of the account, the deal, or the relationship. This means the data layer that supports AI-driven go-to-market needs to be more complete, more unified, and more current than what was sufficient when humans were the primary consumers. The tolerance for missing context is lower because the system consuming the data has no ability to work around what's missing. ## The context graph: what AI actually needs For AI to produce output that is useful for go-to-market, it needs access to a unified data layer that we call a context graph: a continuously updated representation of B2B reality that brings together comprehensive third-party intelligence with an organization's own first-party data. This means combining verified contact and company data with CRM records, call transcripts, email threads, engagement history, intent signals, and the patterns that emerge across thousands of similar accounts and deals. It means resolving entities across all of these sources so that the AI is working with a coherent picture rather than fragmented, conflicting records. And it means structuring that data through a semantic layer so that AI systems can reason over it effectively. Building this kind of context graph requires capabilities that take a long time to develop. Entity resolution across billions of records, continuous data verification, cross-company pattern recognition from tens of thousands of customers, and an ontology designed specifically for go-to-market use cases. These are not capabilities that can be assembled quickly or replicated by connecting a foundation model to a CRM. ## Introducing ZoomInfo MCP We are making ZoomInfo's context graph available to every AI tool through the Model Context Protocol (MCP). MCP is the emerging standard for how AI tools connect to external data sources. The ZoomInfo MCP server allows any MCP-compatible AI tool, whether a commercial assistant like ChatGPT, Claude, or Gemini, or a custom-built enterprise application, to access ZoomInfo's intelligence platform as a grounding layer. What that means in practice: when a seller asks any AI tool a go-to-market question, the response can now draw from ZoomInfo's full data foundation and the intelligence layer that structures and synthesizes across those sources. ## What changes for the seller Consider the difference in a common workflow: call preparation. Without access to go-to-market context, an AI tool responds to "help me prepare for my call with Acme Corp" with a generic framework. It might suggest discovery questions or summarize the company's public profile. The seller still needs to spend 30-45 minutes assembling the actual context they need from multiple systems. With ZoomInfo's context graph connected via MCP, that same question produces a fundamentally different response: the last conversation with the account and what was discussed, the signals firing at the company right now, the relevant contacts and their backgrounds, how similar accounts have moved through the pipeline, and specific recommendations grounded in all of that data. The seller gets the full picture in seconds, synthesized from sources they would never have had time to pull together manually. This is the same value proposition that has driven ZoomInfo's business for nearly 20 years: making sellers the most informed people in the room. The difference is that now, instead of delivering that intelligence through a single application, we are making it available wherever AI is being used for go-to-market work. ## Why this is a strategic bet Our customers are deploying enterprise AI tools across their organizations. They're investing in ChatGPT Enterprise, Claude for teams, Gemini, and their own internal AI applications. These tools are capable, and they are going to become the primary interface through which revenue teams do much of their work. The question for any B2B intelligence provider is whether your data is accessible in those environments. If it is, you become part of the infrastructure that makes AI work for go-to-market. If it isn't, you're a standalone application competing for a shrinking share of the seller's attention. ZoomInfo has spent 20 years building the most comprehensive B2B data foundation in the market: entity resolution across billions of records, cross-company intelligence from 35,000+ customers, continuous verification, and a purpose-built semantic layer for go-to-market use cases. MCP is how we open that foundation to every AI tool in the ecosystem. ## Getting started The ZoomInfo MCP server is available today. Connect through any MCP-compatible client, authenticate with existing ZoomInfo credentials, and data access follows existing package entitlements. > **Try it now** > > [Get started with ZoomInfo MCP](/docs) ================================================================================ # Introduction to GTM AI > An overview of the GTM AI platform — the context foundation that combines proprietary B2B data, entity resolution, a semantic graph, and federated AI models to power modern go-to-market. **Date:** 2025-01-15 **Source:** https://gtm.ai/blog/introduction-to-gtm-ai --- Go-to-market teams are increasingly turning to AI — but generic models lack the context that determines revenue outcomes. CRMs record state changes. AI needs the causal chain. **GTM AI** is the context foundation for AI-powered go-to-market. It connects your data, unlocks AI workflows, and turns every signal into revenue. ## The Context Problem Foundation models are powerful, but they don't know your deals, your accounts, or the signals that matter in your pipeline. When you ask a generic AI to prepare you for a sales call, you get generic advice. When you ask GTM AI, you get actionable context drawn from real relationships, engagements, and signals. > GTM AI doesn't replace your CRM or your data warehouse. It sits alongside them as the semantic layer that makes your data AI-ready. ## Four Integrated Components The platform is built on four tightly integrated layers, each solving a specific piece of the context problem. ### Data Foundations 100M+ companies and 500M+ contacts with attribute-level confidence scoring. The truth layer for GTM AI — every record has provenance and freshness metadata. ### Matching Engine Entity resolution at scale. Match, deduplicate, and link records across first-party CRM data and third-party sources into a single canonical view. ### Context Graph A semantic graph that connects entities, engagements, signals, and relationships. This is the structure that AI reasons over — not flat tables, but a rich web of context. ### AI Models and Agents Federated models and agents purpose-built for GTM tasks: account prioritization, buyer research, deal momentum scoring, next-best-action, and pipeline forecasting. ## How Context Changes GTM Traditional GTM tools give you data. GTM AI gives you **understanding**. | Traditional Approach | With GTM AI | |---------------------|-------------| | Flat CRM records | Rich entity graph with relationships | | Manual research before calls | AI-generated briefs with full deal context | | Static lead scoring | Dynamic momentum scoring based on engagement patterns | | Siloed data across tools | Unified context across all systems | | Generic AI responses | Context-aware intelligence tied to your pipeline | ## Getting Started **Explore the documentation** The [documentation](/docs) covers the platform architecture, each component in depth, and integration guides. **Try the playground** The [AI playground](/playground/ai) lets you interact with GTM AI directly. The [Context Graph Explorer](/playground/context) shows you what your data looks like as a graph. **Browse the marketplace** The [Marketplace](/marketplace) has pre-built data sets and audiences ready to use — from franchise owners to UCC filings to targeted outreach segments. **Connect your tools** GTM AI integrates via MCP (Model Context Protocol), REST APIs, and native connectors. Connect Claude, ChatGPT, Salesforce, and more. > **MCP Integration** > > The fastest way to get started is through the MCP server. Any AI agent that supports MCP can query the Context Graph directly. [See the MCP docs](https://docs.zoominfo.com/docs/mcp). ## What's Next GTM AI is built for the era of AI-native go-to-market. As AI agents become the primary way teams interact with data, the context graph becomes the critical infrastructure layer. Check out the [FAQ](/faq) for common questions, or [request a demo](https://www.zoominfo.com/contact) for a guided walkthrough. ================================================================================ # Marketplace ================================================================================ # Account Executives and Account Managers at Enterprise Companies > Individual contributor AEs and AMs at companies with 1,000+ employees — the practitioners managing complex, multi-stakeholder enterprise deals and key accounts. **Source:** https://gtm.ai/marketplace/account-executives-at-enterprise --- ## Overview Enterprise Account Executives and Account Managers are the revenue-generating engine of large B2B organizations — they manage complex deal cycles, multi-stakeholder relationships, and six-to-seven figure quota targets. With 180,000+ verified enterprise AEs globally, this audience is a large and highly targeted segment for sales productivity tools, training programs, and professional development resources. ## What's Included Verified Account Executive and Account Manager contacts at companies with 1,000+ employees. Direct email and phone with company firmographics for filtering by industry and company size. ## Use Cases - **Sales training and methodology platforms** reaching the practitioners who will champion their tools to management - **Sales productivity and prospecting tools** selling directly to AEs who control tool adoption - **Professional development and certification programs** targeting enterprise AEs investing in their careers ================================================================================ # AI + Cloud Intent Combo > 101,000+ companies showing simultaneous high-intensity research signals for both AI/ML tools and cloud infrastructure — the highest-conviction signal for modern infrastructure vendors. **Source:** https://gtm.ai/marketplace/ai-and-cloud-intent-combo --- ## Overview Companies showing simultaneous intent signals for AI/ML tools and cloud infrastructure are in an active build-out phase — they're modernizing their infrastructure to support AI workloads at the same time. This convergence of intent signals is one of the strongest indicators of near-term purchasing activity for vendors at the intersection of AI and cloud. With 101,000+ companies qualifying, this audience balances precision with scale. ## What's Included Company records with confirmed intent signals across both AI/ML and cloud infrastructure topics, with composite scores for each. Companies meeting both thresholds represent the most actively evaluating segment of the modern infrastructure market. ## Use Cases - **AI infrastructure and GPU cloud vendors** targeting companies standing up AI capacity - **MLOps platforms** reaching companies evaluating cloud-native ML workflows - **Data platform vendors** selling to organizations building AI on cloud-native architecture ================================================================================ # AI Researchers and Applied Scientists > Research Scientists, Applied Scientists, and AI Researchers at companies building AI products and capabilities — the technical thought leaders driving AI innovation. **Source:** https://gtm.ai/marketplace/ai-researchers-and-applied-scientists --- ## Overview Research Scientists and Applied Scientists are the technical vanguard of AI — they're publishing papers, experimenting with new architectures, and often the internal advocates who pull new tools into organizations. With 28,000+ verified AI researchers and applied scientists, this audience is a high-influence segment for compute vendors, research platforms, and AI tooling companies seeking technical champions and early adopters. ## What's Included Verified contacts with Research Scientist, Applied Scientist, AI Researcher, or Principal Scientist titles. Direct email and phone with company firmographics for targeting across AI labs, tech companies, and enterprise AI teams. ## Use Cases - **GPU and compute vendors** reaching scientists with large computational workloads - **Research tooling and experiment tracking platforms** (Weights & Biases, MLflow) targeting hands-on researchers - **AI and ML framework vendors** reaching the practitioners who evaluate and adopt foundational tools ================================================================================ # Angel and Seed Funded Startups > 4,400+ companies that have received Angel or Seed funding within the past two years — early-stage startups at the beginning of their vendor and tooling journey. **Source:** https://gtm.ai/marketplace/angel-and-seed-funded-startups --- ## Overview Angel and Seed funded startups are at the very beginning of their company-building journey — they've raised their first outside capital, are typically 1–20 people, and are making formative decisions about tools, banking, legal, and HR. Vendors who reach them early and offer startup-friendly pricing can establish relationships that scale with the company over years. With 4,400+ qualifying companies in the past two years, this audience represents the top of the venture funnel. ## What's Included Company records with confirmed Angel, Pre-Seed, or Seed funding within the past two years. Funding date and firmographic data included. The audience is concentrated in technology, consumer, fintech, and healthcare verticals. ## Use Cases - **Startup banking and fintech** (Mercury, Brex, Rippling) reaching founders at the moment of their first raise - **Legal and corporate services** targeting founders formalizing their corporate structure post-funding - **SaaS platforms with startup programs** reaching early-stage companies before they commit to competitors ================================================================================ # APAC Enterprise Companies > 22,800+ enterprise companies across Asia-Pacific with 1,000+ employees, verified executive contacts, and regional office data **Source:** https://gtm.ai/marketplace/apac-enterprise-companies --- ## Overview This audience covers 22,800+ enterprise companies across Asia-Pacific with 1,000 or more employees, including coverage across Japan, Australia, Singapore, South Korea, India, and Greater China. Each record includes verified executive contacts, revenue estimates, and country-level headquarters data. The audience is filtered to companies with meaningful organizational scale — eliminating subsidiaries-only records and holding company shells. ## What's Included - **Company Profile**: Company name, website, HQ country, and regional office locations - **Employee Data**: Total headcount and year-over-year growth rate - **Revenue Estimates**: Modeled annual revenue in USD, segmented by APAC sub-region - **Industry Classification**: Primary and secondary NAICS/SIC codes with ZoomInfo taxonomy - **Executive Contacts**: C-suite and VP-level contacts with verified email and direct phone ## Use Cases ### APAC Market Entry and Expansion Identify the largest enterprise companies in target APAC markets before committing field resources. Filter by country, industry, and employee count to build a realistic TAM and prioritize entry sequence. ### Enterprise Software Sales in Asia Reach technology decision-makers at the 22,800+ APAC enterprises most likely to carry enterprise software budgets. Verified executive contacts reduce the cost of list-building in markets where data quality is inconsistent. ### Cross-Border Partnership Development Source business development and partnership contacts at APAC enterprises for distribution agreements, OEM relationships, and co-sell arrangements. Country and industry filters allow precise targeting by partner profile. ### Regional Distribution and Channel Sales Map APAC enterprises by sub-region and vertical to build regional channel strategies. Employee growth data surfaces accounts expanding into new markets — a strong signal for near-term budget availability. ================================================================================ # Atlassian + Slack Stack > 11,800+ companies running both Atlassian and Slack — engineering-led organizations with a modern developer workflow built on the two most widely adopted engineering and communication tools. **Source:** https://gtm.ai/marketplace/atlassian-slack-stack --- ## Overview Atlassian + Slack is the default engineering-led collaboration stack — Jira tracks work while Slack drives communication, and the most effective engineering teams have deep integrations between the two. Companies running both have invested in modern developer workflows and are prime buyers for tools that extend, automate, or improve the Jira-Slack experience. With 11,800+ confirmed dual installations, this audience targets the heart of the developer tooling ecosystem. ## What's Included Confirmed installations of both Atlassian and Slack with company firmographics. The audience spans software companies, agencies, and tech-forward enterprises that run engineering-centric development workflows. ## Use Cases - **Incident management platforms** (PagerDuty, Opsgenie) selling to teams managing on-call workflows across Jira and Slack - **CI/CD and deployment automation tools** targeting engineering teams where Jira and Slack are the integration layer - **Engineering analytics and DORA metrics platforms** reaching organizations with structured Jira data and Slack communication ================================================================================ # AWS + Snowflake Stack > 16,000+ companies running both AWS and Snowflake — cloud-native data organizations that have standardized on Amazon's infrastructure with Snowflake as their analytics layer. **Source:** https://gtm.ai/marketplace/aws-snowflake-stack --- ## Overview Companies running both AWS and Snowflake have built their data infrastructure on the two most widely adopted cloud and analytics platforms — a combination that defines the modern cloud-native data stack. These 16,000+ organizations are active buyers of the tools that bridge, extend, and optimize that stack: data pipelines, governance, cost management, and AI tooling built for cloud-native environments. ## What's Included Confirmed installations of both AWS and Snowflake with company firmographics. The audience skews toward data-intensive technology, financial services, and healthcare companies with significant cloud infrastructure investment. ## Use Cases - **AWS-native data tools** (Glue, EMR partners, S3-based pipelines) selling to companies that have standardized on the AWS-Snowflake data path - **FinOps and cloud cost optimization vendors** targeting companies managing dual AWS + Snowflake spend - **Data governance and security platforms** reaching organizations protecting data across AWS storage and Snowflake analytics layers ================================================================================ # Azure + Snowflake Stack > 10,800+ companies running both Microsoft Azure and Snowflake — enterprises that have chosen Azure as their cloud backbone while using Snowflake for analytics and data sharing. **Source:** https://gtm.ai/marketplace/azure-snowflake-stack --- ## Overview Companies running Azure and Snowflake together represent a specific and high-value profile: they're often enterprise organizations that standardized on Microsoft infrastructure but chose Snowflake over Azure Synapse for their analytics workloads. This 10,800+ audience is a strong signal for vendors selling data integration, governance, and analytics tools that need to work across both ecosystems. ## What's Included Confirmed installations of both Azure and Snowflake alongside company firmographics. The audience skews toward mid-to-large enterprises in regulated industries — financial services, healthcare, and manufacturing — that run Microsoft infrastructure at their core. ## Use Cases - **Azure Data Factory and integration partners** selling pipelines that move data into Snowflake - **Data governance platforms** covering both Azure Active Directory/Purview and Snowflake data assets - **BI and analytics vendors** reaching companies querying Snowflake from an Azure-native analytics environment ================================================================================ # B2B SaaS Companies Globally > 330k+ B2B SaaS companies worldwide with employee count, revenue estimates, funding data, tech stack, and decision-maker contacts **Source:** https://gtm.ai/marketplace/b2b-saas-companies-globally --- ## Overview This is the most comprehensive B2B SaaS company list available globally, built from ZoomInfo's identity graph and continuously refreshed against web signals, job postings, and firmographic data. It covers pure-play SaaS vendors, vertical software companies, and infrastructure providers across every geography and market segment. Anyone selling to software companies — from infrastructure tools to developer platforms to revenue tech — starts here. ## What's Included - **Company Identity**: Verified company name, primary domain, and headquarters location - **Headcount Signals**: Current employee count with 1-year and 3-year growth rates by department - **Revenue Estimates**: Model-derived ARR and total revenue ranges based on headcount, pricing, and market data - **Funding Profile**: Stage (seed through growth equity), total capital raised, and most recent round date - **Tech Stack**: Software tools detected across sales, marketing, engineering, and operations functions - **Decision-Maker Contacts**: Verified email and phone for C-suite, VP, and director-level buyers ## Use Cases ### SaaS Platform Sales Target the 330k+ SaaS companies by funding stage, employee count, or tech stack to build precise ICP segments. Filter to companies using competing or complementary tools to prioritize displacement and expansion plays. ### Partnership and Integration Development Identify potential integration partners by filtering on tech stack overlap. Companies already using adjacent tools in your ecosystem are 3–5x more likely to evaluate and adopt a native integration. ### Competitive Intelligence Map the SaaS landscape by sub-category, funding stage, and geographic market to identify where competitors are gaining traction and where whitespace exists. Layer in headcount growth to identify which competitors are scaling fastest. ### Investor Deal Sourcing Scan the full universe of funded SaaS companies for deal sourcing, portfolio benchmarking, or pre-pitch research. Filter by stage, vertical, geography, and growth velocity to surface companies matching specific investment theses. ================================================================================ # Biotech and Life Sciences Companies in the US > 229,000+ US biotech, pharmaceutical, and life sciences companies with pipeline data, funding history, and executive contacts **Source:** https://gtm.ai/marketplace/biotech-life-sciences-us --- ## Overview The US life sciences sector spans small biotech startups through global pharmaceutical companies, with shared requirements for specialized software, regulatory expertise, and scientific infrastructure. This audience covers 229,000+ US companies across biotech, pharmaceutical, medical devices, diagnostics, and contract research organizations — enriched with sub-sector classification, funding history, R&D focus, and verified executive and scientific leadership contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters - **Sub-Sector Classification**: Biotech, pharmaceutical, medical devices, diagnostics, CRO, or CDMO - **Funding Profile**: Stage, total raised, and most recent round date (including NIH grants for academic spinouts) - **Headcount**: Employee count and 1-year growth rate - **R&D Focus**: Therapeutic area or scientific domain where available (oncology, immunology, genomics, etc.) - **Leadership Contacts**: Verified email and phone for C-suite, VP of R&D, VP of Clinical Operations, and CSO contacts ## Use Cases ### Life Sciences Platform Sales ERP, LIMS, clinical data management, and regulatory affairs platforms have a defined buyer set within this audience. Sub-sector classification and employee count allow vendors to focus on the stage and type of company most likely to need their specific solution. ### Clinical Research and CRO Services CROs and clinical service vendors can use therapeutic area focus and pipeline stage to identify biotech and pharmaceutical companies actively running or preparing for clinical trials. Funding data helps prioritize companies with capital to deploy on outsourced research. ### Healthcare Technology Sales Electronic lab notebooks, trial management software, supply chain tools, and quality management systems all have distinct buying cycles within this sector. Use company size and sub-sector to target the right buyer with the right product positioning. ### Investor Deal Sourcing Life sciences investors can filter by sub-sector, therapeutic area, and funding stage to surface companies that fit specific portfolio mandates. Headcount growth and recency of funding provide additional signals for deal prioritization. ================================================================================ # Biotech Companies in Boston > 2,400+ biotech, pharmaceutical, and life sciences companies in the Greater Boston area — the world's leading life sciences cluster **Source:** https://gtm.ai/marketplace/biotech-companies-boston --- ## Overview Greater Boston — anchored by Kendall Square in Cambridge — is the world's most concentrated life sciences cluster, housing over 2,400 biotech, pharmaceutical, genomics, medical device, and life sciences technology companies. This audience is enriched with life sciences sub-sector classification, funding stage and total capital raised, clinical pipeline stage where available, and academic or hospital affiliations (MIT, Harvard, MGH, Dana-Farber, Broad Institute). Executive and scientific leadership contacts include both business leadership (CEO, CFO, CSO) and operational decision-makers. ## What's Included - **Company Identity**: Company name, website, and Cambridge/Boston location - **Sub-Sector**: Life sciences classification (biopharmaceutical, genomics/genomic medicine, medical device, diagnostics, digital health, CRO/CDMO, life sciences tools) - **Funding and Stage**: Funding stage, total raised, and clinical pipeline stage (preclinical, Phase I/II/III, commercial) - **Affiliations**: Academic and hospital spin-out affiliations (MIT, Harvard, Broad Institute, MGH, Dana-Farber) - **Contacts**: CEO, CFO, CSO, VP R&D, and operational leadership with verified email and phone ## Use Cases ### Life Sciences Platform and CRO Sales Contract research organizations, data platforms, clinical trial management systems, and regulatory compliance tools target Boston biotech companies at specific pipeline stages — preclinical companies have different needs than Phase III or commercial-stage organizations. Pipeline stage enrichment enables stage-appropriate outreach. ### Lab Equipment and Reagent Sales Scientific instrument vendors, lab automation providers, and reagent suppliers target Boston biotech companies by company stage and size. Early-stage companies are high-volume purchasers of consumables; growth-stage companies are capital equipment buyers. Funding data helps segment by purchasing power and procurement formality. ### Boston Biotech Investor Deal Sourcing Life sciences venture capital firms and crossover investors active in Boston can use funding stage and pipeline stage data to identify companies approaching the next funding milestone — preclinical companies nearing IND filing, Phase II companies approaching Phase III decisions — for proactive deal sourcing. ### Scientific Recruiting and Talent Acquisition Life sciences executive search firms and talent acquisition platforms can identify companies in active hiring phases — growth-stage companies post-funding, clinical-stage companies building commercial teams — using funding recency and headcount growth as hiring signal proxies. ================================================================================ # Build List > Describe your target audience in plain language and get a structured, exportable table of matching contacts or companies from ZoomInfo's database. **Source:** https://gtm.ai/marketplace/zoominfo-build-list --- ## Overview The Build List skill turns a natural language description of your target audience into a structured, exportable table of real contacts or companies from ZoomInfo's database. No query builders, no form fields — just describe what you're looking for and get a clean list back. This skill handles the entire workflow: parsing your criteria into structured filters, validating every filter value against ZoomInfo's lookup API, executing the search with optimal sort parameters, and enriching sparse results with emails, direct dials, and accuracy scores. The result is a ready-to-use table artifact you can copy, download, or feed directly into your next step. Whether you need 25 highly targeted contacts for a personalized sequence or 500 accounts for a broad ABM wave, Build List adapts to the scope and outputs a summary of exactly what filters were applied — so you can verify the list matches your intent and iterate if needed. ## What It Does - **Natural language parsing**: Translates conversational criteria ("Series B SaaS startups in Austin with 50–200 employees using Salesforce") into structured ZoomInfo filter parameters - **Filter validation**: Looks up valid values and IDs for every filter before searching — never guesses, so results are accurate - **Contact and company search**: Runs searches against ZoomInfo's full database with optimal sort ordering (contact accuracy score, company revenue or headcount) - **Enrichment pass**: Fetches emails, direct dials, and additional profile data for top results to fill in any gaps from the initial search - **Refinement guidance**: When results are too broad or narrow, suggests specific filter adjustments with estimated impact on list size ## Use Cases ### Outbound Campaign Lists Give Claude your ICP criteria — title, seniority, industry, company size, location, tech stack — and get a contact list formatted for export to your sequencing tool or CRM. Ask for 25 hyper-targeted contacts for a personalized campaign or 250 for broader coverage. ### Account-Based Marketing Describe the firmographic profile of your target accounts (industry, revenue band, employee count, geography, business model) and get a company list with contacts pre-mapped. Iterate on filters in conversation until the segment matches your ABM program requirements. ### Territory Planning Build a comprehensive view of the addressable market in a new territory by generating lists filtered by metro region, company size, and vertical. Export the results and load them into your CRM to assign to reps. ================================================================================ # C-Suite Executives with Verified Email > 5,500,000+ C-suite executives globally with verified email addresses — the broadest executive contact layer in ZoomInfo's database **Source:** https://gtm.ai/marketplace/c-suite-with-verified-email --- ## Overview With 5.5 million contacts globally, this audience is the largest and most comprehensive C-suite contact layer in ZoomInfo's database — filtered specifically for verified email addresses. It spans CEOs, CFOs, COOs, CTOs, CMOs, CROs, CHROs, and other executive titles across every industry, geography, and company size tier. Email verification is performed through ZoomInfo's real-time validation engine, ensuring deliverability and reducing bounce rates that degrade domain reputation in high-volume outbound programs. ## What's Included - **Identity**: Full name and verified email address - **Role Context**: C-suite title and functional area (CEO, CFO, CTO, CMO, COO, CRO, CHRO, etc.) - **Company Context**: Company name, industry, and company type (public, private, PE-backed, nonprofit) - **Company Size**: Employee count and revenue range - **Geography**: HQ location by country, state/region, and city - **Social**: LinkedIn profile URL ## Use Cases ### Executive ABM and Outbound Campaigns Account-based programs targeting C-suite require high-confidence email addresses to avoid deliverability degradation across large sends. This audience provides verified email at scale — enabling ABM teams to build executive contact layers across target account lists without manual research or data appends. ### Board and Leadership Relationship Mapping Revenue leaders and strategic account teams building relationship maps for enterprise accounts need complete executive coverage. This audience provides the full C-suite contact layer across a target account list, enabling gap analysis between known contacts and full executive coverage. ### C-Suite Event and Community Outreach Executive dinners, roundtables, peer community programs, and analyst events require targeted outreach to a specific executive profile — by industry, company size, or functional role. Verified email enables invitation sequences that reach the executive directly rather than routing through an EA. ### Executive Gifting and Direct Mail Programs Physical gifting and direct mail programs targeting C-suite decision-makers require accurate name, title, and company data even when physical address is not available. Verified email supports multi-channel sequences where email is the primary fulfillment and tracking mechanism. ================================================================================ # CEOs and Founders of Small Businesses > 2,200,000+ CEOs, owners, and founders at companies with 10–50 employees — the decision-maker and budget-holder in a single contact **Source:** https://gtm.ai/marketplace/ceos-small-business --- ## Overview This audience covers 2,200,000+ CEOs, owners, and founders at companies with 10 to 50 employees — the segment where the top executive is simultaneously the decision-maker, budget holder, and primary buyer for nearly every vendor relationship. At this company size, there is no procurement layer, no IT committee, and no multi-stakeholder approval process. Industry and HQ location data enable vertical and geographic segmentation at scale, which is essential for SMB campaigns where message relevance drives response rate more than contact quality alone. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Title Classification**: Standardized role label — CEO, Owner, Founder, or President - **Company Data**: Company name, employee count, industry, and business category - **Location Data**: HQ city, state, country, and zip code for geographic targeting - **Digital Presence**: Company website and available social profiles ## Use Cases ### SMB Software and SaaS Sales Reach the owner or CEO who signs every software contract at companies with 10–50 employees. At this scale, the CEO is the de facto IT, finance, and operations buyer — making single-contact outreach significantly more efficient than multi-threaded enterprise campaigns. ### Small Business Financial Services Target small business CEOs and owners for lending, business banking, payroll, and financial services products. Industry and employee count data enable segmentation by business type and likely financial product fit. ### Insurance and Benefits Products Identify business owners responsible for selecting health insurance, commercial liability, and employee benefits plans. The 10–50 employee band is the primary market for small group health plans and commercial insurance products. ### Local and Regional Service Sales Segment by HQ location to build hyper-local outreach campaigns for regional service businesses — staffing, facilities, accounting, marketing agencies, and professional services. Geographic precision is a differentiating capability in SMB markets where national lists underperform local relevance. ================================================================================ # CEOs at Series A Companies > 3,100+ founders and CEOs at companies that have closed a Series A round within the past two years — the prime window for vendor adoption and team-building. **Source:** https://gtm.ai/marketplace/ceos-at-series-a-companies --- ## Overview The twelve months following a Series A close represent one of the highest-velocity purchasing windows in the B2B market. CEOs and founders at newly Series A-funded companies are actively hiring, signing contracts, and building out their core stack. This audience targets that exact window — 3,100+ CEOs at companies that have raised their Series A in the past two years. ## What's Included Records combine CEO contact details with company-level funding data, including the Series A date, amount, and current employee count. All contacts are verified with direct work email and phone where available. ## Use Cases - **SaaS vendors** reaching decision-makers before the stack solidifies - **Recruiting firms** targeting CEOs who are actively hiring to scale post-funding - **Financial services and legal firms** serving newly funded companies with banking, accounting, or compliance needs ================================================================================ # CFOs at Enterprise Companies > 60,700+ Chief Financial Officers at companies with 1,000+ employees — enterprise finance decision-makers with complex multi-system budgets and significant vendor spend. **Source:** https://gtm.ai/marketplace/cfos-at-enterprise-companies --- ## Overview Enterprise CFOs oversee complex, multi-entity finance operations — they're managing consolidations, multi-currency reporting, treasury strategy, and investor relations alongside internal finance operations. With 60,700+ verified CFOs at companies with 1,000+ employees, this is the premier audience for enterprise financial software, banking services, and finance consulting. ## What's Included Verified CFO contacts at companies with 1,000 or more employees. Direct email and phone with company firmographics for targeting by industry, revenue, and geography. ## Use Cases - **Enterprise ERP and financial management vendors** (SAP, Oracle, Workday Finance) reaching the primary finance executive buyer - **Treasury and capital markets technology** targeting CFOs responsible for liquidity and risk management - **Big 4 and consulting firms** prospecting CFOs at large companies seeking financial transformation advisory ================================================================================ # CFOs at Mid-Market Companies > 85,500+ Chief Financial Officers at companies with 200–2,000 employees — verified contacts, company financials, and ownership context **Source:** https://gtm.ai/marketplace/cfos-at-mid-market-companies --- ## Overview This audience covers 85,500+ CFOs at mid-market companies with 200 to 2,000 employees — the segment where the CFO is both the financial decision-maker and a direct influencer on technology and vendor spending. Ownership type segmentation (private, public, or PE-backed) is critical context: PE-backed CFOs operate under distinct reporting, compliance, and efficiency pressures that change buying behavior relative to founder-led or public company peers. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and tenure at current company - **Company Financials**: Revenue estimates, employee count, and funding or ownership status - **Ownership Type**: Private, public, PE-backed, or family-owned classification - **Company Profile**: Industry, HQ location, and company website ## Use Cases ### Financial Software and ERP Sales Target mid-market CFOs actively managing the tradeoffs between legacy finance systems and modern ERP or FP&A platforms. Ownership type filtering lets you separate PE-backed accounts — where standardization and reporting efficiency are explicit mandates — from privately held companies on a different adoption curve. ### Accounting and Audit Services Reach CFOs at mid-market companies that carry audit requirements but may lack the internal capacity to manage them efficiently. Revenue and employee count filters help identify companies at the inflection point where external audit and advisory services become cost-effective. ### CFO Advisory and Consulting Source leads for fractional CFO services, financial advisory engagements, and strategic finance consulting. The 200–2,000 employee band is the primary market for engagements where a full-time VP of Finance or CFO handles day-to-day but needs outside perspective. ### Banking and Credit Services Identify CFOs at mid-market companies likely to be evaluating credit facilities, treasury management, or banking relationships. Revenue estimates and ownership type provide the financial profile needed to segment by creditworthiness and service fit. ================================================================================ # Chief Data Officers and VP Data > 30,400+ Chief Data Officers, VPs of Data, and Heads of Data — the executive buyers responsible for data strategy, infrastructure, and governance investments. **Source:** https://gtm.ai/marketplace/chief-data-officers --- ## Overview The Chief Data Officer and VP of Data role has emerged as the primary executive champion for data infrastructure, governance, and analytics investments. With 30,400+ verified data leaders globally, this audience represents the decision-making tier for a wide range of data platform purchases — from cloud warehouses and data catalogs to governance tools and AI platforms. ## What's Included Verified contacts with CDO, Chief Data Officer, VP of Data, or Head of Data titles, including direct contact information. Company firmographics support segmentation by industry and size to match your data buyer profile. ## Use Cases - **Data platform vendors** (Snowflake, Databricks partners, cloud warehouse) reaching data executive buyers - **Data governance and catalog platforms** selling to the role responsible for data quality and standards - **AI and ML platforms** targeting CDOs responsible for enterprise AI strategy and ROI ================================================================================ # Chief People Officers and VP of People > 67,600+ Chief People Officers, CHROs, VP of People, and Head of People leaders — the executive human resources buyers responsible for talent strategy and the people tech stack. **Source:** https://gtm.ai/marketplace/chief-people-officers --- ## Overview The Chief People Officer and CHRO role has grown from administrative HR to strategic business partnership — these leaders own talent strategy, employer brand, compensation philosophy, and people technology investments. With 67,600+ verified people leaders globally, this audience is essential for any HR tech vendor targeting the executive buyer tier. ## What's Included Verified contacts with Chief People Officer, CHRO, VP of People, or Head of People titles. Direct email and phone with company firmographics for targeting across company sizes and industries. ## Use Cases - **HRIS, HCM, and HR platform vendors** reaching the executive sponsor for people technology investments - **Employee engagement and culture platforms** selling to leaders responsible for workforce experience - **Compensation benchmarking and total rewards platforms** targeting the people leaders who own comp strategy ================================================================================ # Chief Revenue Officers and Revenue Leaders > 25,000+ CROs, Chief Revenue Officers, and revenue leadership contacts globally — the single owner of the top-line number at their company **Source:** https://gtm.ai/marketplace/cros-and-revenue-leaders --- ## Overview The CRO owns the complete revenue motion — new logo, expansion, and retention — making them the broadest and highest-value executive buyer in the go-to-market technology stack. This audience covers 25,000+ CROs and Chief Revenue Officers globally, including title variants like Head of Revenue, SVP Revenue, and President of Revenue. Records include ARR range context, org scope signals, and verified revenue tech stack data to help revenue teams identify the highest-fit targets within this audience. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **Title Variants**: CRO, Chief Revenue Officer, Head of Revenue, SVP Revenue, President of Revenue, EVP Revenue — all included - **Company ARR Context**: ARR range estimates based on headcount, funding stage, and industry benchmarks - **Org Scope**: Sales team size and CS team size signals where available — indicates the breadth of the CRO's ownership - **Revenue Tech Stack**: Known CRM, sales engagement, forecasting, and CS platforms from technographic data - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Revenue Platform and Intelligence Sales Revenue intelligence platforms, forecasting tools, and pipeline analytics vendors target CROs as the primary economic buyer for the revenue stack. CROs evaluate platforms based on visibility into pipeline health, forecast accuracy, and rep performance — lead with outcomes (pipeline conversion rates, forecast accuracy improvements) rather than features. Filter by ARR range to align your platform's pricing to the CRO's budget authority. ### Sales and CS Alignment Tooling CROs who own both sales and customer success are actively buying tools that bridge the new logo and expansion revenue motion — customer health scoring, expansion playbooks, handoff workflows, and unified revenue data platforms. Use org scope signals to identify CROs with explicit CS ownership, as these are the buyers most motivated by full-funnel revenue alignment tooling. ### CRO Advisory and Consulting Revenue consulting firms, fractional CRO services, and GTM advisory practices target CROs who are building or rebuilding their revenue motion. CROs at companies in the $5M–$50M ARR range are frequently looking for external frameworks for territory design, quota setting, comp plan architecture, and forecasting methodology. Use ARR range and funding stage to prioritize CROs at companies in active GTM scale-up mode. ### Executive Peer Community Outreach CRO peer communities, revenue leadership roundtables, and executive advisory boards target this audience for membership recruitment and thought leadership distribution. Revenue leadership is a relatively young function — many CROs actively seek peer benchmarks on metrics, org design, and technology choices. Invitations to curated CRO peer communities see strong acceptance rates, particularly for communities organized around company stage or industry vertical. ================================================================================ # CIOs and VP of IT > 102,000+ Chief Information Officers and VP-level IT leaders — the executive technology buyers responsible for enterprise infrastructure, vendor relationships, and IT strategy. **Source:** https://gtm.ai/marketplace/cios-and-vp-it --- ## Overview The CIO and VP of IT sit at the top of enterprise technology decision-making — they own infrastructure budgets, vendor relationships, and IT strategy. With 102,000+ verified IT executives globally, this audience spans the full range from SMB IT directors with broad responsibility to Fortune 500 CIOs managing thousands of technologies and hundreds of staff. ## What's Included Verified contacts with CIO, Chief Information Officer, VP of Information Technology, VP IT, or Head of IT titles. Direct email and phone with company firmographics for segmentation by industry, company size, and geography. ## Use Cases - **Enterprise hardware and infrastructure vendors** reaching executive IT buyers who control capital budgets - **IT service management and ITSM platforms** targeting CIOs responsible for internal IT operations - **Cloud migration and managed services providers** selling IT transformation to the executive sponsor ================================================================================ # CISOs and VP Security > 27,000+ Chief Information Security Officers and VP-level security leaders — the primary decision-makers for enterprise cybersecurity purchasing. **Source:** https://gtm.ai/marketplace/cisos-and-vp-security --- ## Overview The CISO and VP Security role is the definitive decision-maker for enterprise cybersecurity investment. With 27,000+ verified security leaders across organizations of all sizes, this audience captures the full spectrum of the security buyer — from startup CISOs wearing multiple hats to enterprise security VPs managing hundred-person teams and multi-million-dollar budgets. ## What's Included Verified contacts with CISO, Chief Information Security Officer, or VP Security titles, including direct email and phone. Company firmographics enable targeting by industry, size, and geography to align with your ideal security customer profile. ## Use Cases - **Cybersecurity vendors** (endpoint, identity, network, cloud security) reaching their primary buyer - **Security awareness and training platforms** targeting CISOs responsible for culture and human risk - **vCISO and security consulting services** reaching companies that may need fractional security leadership ================================================================================ # CISOs at Enterprise Companies > 8,400+ Chief Information Security Officers at companies with $500M+ revenue — verified contacts, org context, and security stack signals **Source:** https://gtm.ai/marketplace/cisos-at-enterprise-companies --- ## Overview This audience covers 8,400+ CISOs at companies with $500M or more in annual revenue — the decision-maker population for enterprise security budgets. Each record includes time-in-role data to distinguish newly appointed CISOs (high receptivity to new vendor evaluation) from entrenched incumbents, along with known security technology stack to identify existing vendor relationships and displacement opportunities. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, tenure, and time in role at current company - **Company Data**: Company name, revenue tier, industry, and employee count - **Security Stack**: Known security tooling across endpoint, SIEM, identity, and cloud security categories - **Team Context**: Estimated security team headcount and reporting structure ## Use Cases ### Security Platform and Tooling Sales Reach enterprise CISOs who own the budget for security platform decisions. Time-in-role filtering surfaces newly appointed CISOs in their first 12 months — a proven signal for open vendor evaluation cycles. ### MSSP and Managed Security Services Target CISOs at companies with security team gaps or known tooling limitations. Revenue tier and team size data helps segment by organizations most likely to outsource detection, response, or compliance functions. ### Compliance and Risk Consulting Identify CISOs at enterprises in regulated industries — financial services, healthcare, government contractors — where compliance mandates drive structured advisory and audit budgets. ### Board-Level Security Advisory Services Reach senior security leaders at the largest enterprises for fractional CISO services, board advisory programs, and executive security briefings. Revenue filters ensure focus on organizations with governance-level security investment. ================================================================================ # Climate Tech and Clean Energy Companies > 916,000+ energy and clean technology companies globally with funding data, employee count, and executive contacts **Source:** https://gtm.ai/marketplace/climate-tech-clean-energy-companies --- ## Overview The clean energy and climate technology sector spans hardware manufacturers, software platforms, project developers, and service providers — unified by their role in the energy transition and exposure to the same regulatory, financial, and operational dynamics. This audience covers 916,000+ companies globally across solar, wind, energy storage, grid technology, carbon markets, and energy efficiency, with verified funding data and executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and global headquarters - **Energy Sub-Sector**: Solar, wind, energy storage, grid technology, energy efficiency, carbon markets, or electric vehicles - **Funding Profile**: Stage, total raised, and most recent round date (including government grants and DOE funding) - **Headcount**: Employee count with 1-year growth rate - **Revenue Estimates**: Model-derived revenue ranges for private companies - **Executive Contacts**: Verified email and phone for C-suite and VP-level contacts ## Use Cases ### Clean Energy Technology Sales Software, data, and operational technology vendors targeting clean energy companies can use sub-sector classification and company size to identify the right buyer. A utility-scale solar developer has different software needs than a residential energy efficiency startup. ### Climate-Focused Investor Sourcing Climate tech investors can filter by sub-sector, funding stage, and geography to surface companies aligned with specific investment mandates. Revenue estimates and headcount growth provide additional signals for evaluating traction relative to capital raised. ### Infrastructure and Project Finance Banks, infrastructure funds, and project finance advisors can use this audience to identify companies developing large-scale clean energy projects requiring debt, tax equity, or structured finance. Company size and revenue estimates help prioritize the highest-volume targets. ### Policy and Regulatory Consulting Consulting firms and law practices specializing in energy regulation, permitting, and IRA incentive monetization can use sub-sector and geography to identify the companies most actively navigating the policy landscape and most likely to need specialized advisory services. ================================================================================ # Cloud Architects at Enterprise Companies > 14,000+ Cloud Architects at companies with 1,000+ employees — verified contacts, cloud platform, and infrastructure context **Source:** https://gtm.ai/marketplace/cloud-architects-enterprise --- ## Overview This audience covers 14,000+ Cloud Architects at companies with 1,000 or more employees — the technical leaders responsible for cloud platform strategy, reference architecture, and infrastructure governance at enterprise scale. Known cloud platform data across AWS, Azure, and GCP enables ecosystem-specific sales and partnership outreach: a Cloud Architect running a multi-account AWS organization has different tooling needs than one managing Azure enterprise agreements or a GCP-native data platform. Architecture specialization data distinguishes cloud security architects, data architects, and network architects from generalist cloud platform roles. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, architecture specialization, seniority level, and tenure - **Company Data**: Company name, employee count, industry, and HQ location - **Cloud Platform**: Primary cloud provider (AWS, Azure, GCP) and multi-cloud indicators - **Architecture Scope**: Specialization area — security, data, network, applications, or platform ## Use Cases ### Cloud Platform and Managed Services Sales Reach Cloud Architects who influence or directly own cloud platform vendor relationships at enterprise companies. Cloud platform data enables provider-specific outreach — AWS marketplace partners, Azure CSP programs, and GCP partner network vendors each benefit from segmenting by primary cloud environment. ### Infrastructure Tooling and FinOps Sales Target Cloud Architects at enterprises where cloud spend optimization, cost governance, and resource management are active initiatives. Employee count filters identify organizations with the cloud footprint size where FinOps platforms and infrastructure tooling provide measurable ROI. ### Cloud Architecture Recruiting and Staffing Source Cloud Architects at enterprise companies for senior architecture, principal engineer, and cloud center of excellence roles. Specialization and cloud platform data enable recruiters to match candidates to specific open roles requiring AWS certifications, Azure expertise, or GCP data platform experience. ### Multi-Cloud and Hybrid Cloud Advisory Identify Cloud Architects at large enterprises managing multi-cloud or hybrid environments for consulting, integration, and advisory services. Multi-cloud indicators surface the accounts where cloud complexity creates the greatest demand for outside architectural expertise. ================================================================================ # CMOs at B2B SaaS Companies > 18,400+ Chief Marketing Officers at B2B SaaS companies — verified contacts, company ARR range, and marketing tech stack **Source:** https://gtm.ai/marketplace/cmos-at-b2b-saas-companies --- ## Overview This audience covers 18,400+ CMOs at B2B SaaS companies, where marketing investment is directly tied to pipeline and revenue targets rather than brand spend. Each record includes the company's ARR range, funding stage, and known marketing technology stack — giving sellers and agencies the context to lead with relevant value before the first conversation. Growth rate signals surface companies scaling marketing headcount and tooling in parallel. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, tenure, and seniority level - **Company Data**: Company name, ARR range estimate, and funding stage - **Marketing Tech Stack**: Known tools across MAP, CRM, ABM, content, and analytics categories - **Growth Signals**: Year-over-year headcount growth rate and recent funding events ## Use Cases ### MarTech Platform and Data Sales Target CMOs at B2B SaaS companies with confirmed gaps in their marketing stack. Tech stack data identifies companies missing key capabilities — ABM coverage, intent data, or attribution — making cold outreach directly relevant from the first message. ### Agency and Creative Services Source CMOs at B2B SaaS companies in the ARR range where marketing agencies and production partners are most commonly engaged. Growth rate filtering surfaces companies ramping marketing investment and likely to be evaluating new agency relationships. ### Demand Gen and ABM Tool Sales Reach CMOs actively running demand gen and account-based programs who are evaluating or replacing core ABM, enrichment, and engagement platforms. Funding stage filters help prioritize companies with fresh capital and active tech stack investment cycles. ### CMO Peer Community and Advisory Build invite lists for executive CMO roundtables, research panels, and peer advisory programs. ARR and funding stage segmentation ensures community participants share comparable scale, challenges, and go-to-market context. ================================================================================ # Community and Developer Relations Leaders > Head of Developer Relations, VP Community, Developer Advocate leaders, and Community Managers — the professionals building developer ecosystems and technical communities for software companies. **Source:** https://gtm.ai/marketplace/community-and-devrel-leaders --- ## Overview Developer Relations and Community leaders are the architects of ecosystem-led growth — they build developer documentation, run hackathons, manage Discord communities, and create the feedback loops between technical users and product teams. With 12,000+ verified DevRel and community professionals, this audience represents the growing function responsible for community-led and developer-led growth strategies at technology companies. ## What's Included Verified contacts with Head of Developer Relations, VP Community, Developer Advocate, Senior DevRel, or Community Manager (at senior level) titles. Direct email and phone with company firmographics. ## Use Cases - **Developer community and documentation platforms** selling to the professionals who build and manage them - **Community management tools** (Circle, Discord for Developers) targeting DevRel and community leaders - **API and developer experience vendors** building partnerships with DevRel leaders at ecosystem companies ================================================================================ # Companies Showing AI and ML Buying Intent > 105,500+ companies actively researching AI, machine learning, and generative AI solutions — real-time intent signals updated weekly **Source:** https://gtm.ai/marketplace/companies-showing-ai-intent --- ## Overview This is a signal audience — companies are actively consuming AI and ML content right now. Records are built from real-time behavioral signals across research sites, vendor review platforms, job postings, and content consumption patterns. The COMPOSITE_SCORE reflects research intensity across multiple AI and ML topics. This audience is best used for timing outreach to active evaluation cycles — not as a static list, but as a live feed of in-market buyers. ## What's Included - **Company Identity**: Name, website, industry, and HQ location - **Intent Topics**: Specific AI and ML research categories driving the signal (generative AI, LLMs, MLOps, data labeling, vector databases, etc.) - **Signal Score**: Composite score reflecting research intensity relative to historical baseline - **Signal Timing**: Date of most recent signal spike and trend direction (rising, stable, declining) - **Company Profile**: Employee count, revenue range, and industry classification - **Recommended Contacts**: Prioritized contacts in data science, engineering, and product roles for outreach ## Use Cases ### AI and ML Platform Sales Companies actively researching AI platforms are often weeks away from issuing an RFP or starting a proof-of-concept. Use signal score and trend direction to prioritize outreach — companies with rising signals are earlier in the cycle; companies with recent spikes may be in active evaluation. Filter by industry to align with your platform's strongest use cases. ### Generative AI Tool and API Sales The generative AI market is moving fast and buyers are evaluating multiple options simultaneously. This audience surfaces companies researching LLM APIs, prompt engineering tools, RAG architectures, and AI application development platforms. Filter by company size to separate enterprise buyers (who need contracts, SLAs, and security reviews) from SMB buyers (who self-serve and close faster). ### AI Consulting and Implementation Services Consulting firms and system integrators can use this audience to identify companies that are early in their AI strategy — researching the space but not yet committed to a vendor. These are pre-sale conversations where advisory relationships are established. Filter by company size and industry to align with your delivery team's expertise. ### Data Infrastructure and MLOps Sales AI intent often co-occurs with data infrastructure and MLOps research signals. Companies building AI capabilities need data pipelines, feature stores, model monitoring, and deployment infrastructure. Use this audience in combination with cloud intent signals to find companies actively building their AI stack from the ground up. ================================================================================ # Companies Showing Cloud Migration Intent > 9,000+ companies actively researching cloud migration, hybrid cloud, and cloud strategy — real-time intent signals across major cloud platforms **Source:** https://gtm.ai/marketplace/companies-showing-cloud-intent --- ## Overview Cloud migration intent signals indicate companies actively researching lift-and-shift migrations, data center consolidation, hybrid cloud strategies, and multi-cloud architectures. This audience covers 9,000+ companies with elevated research activity across AWS, Azure, GCP, and cloud-adjacent topics like containerization, Kubernetes, and cloud networking. Intent signals are combined with job posting patterns to surface companies that are building cloud teams alongside their evaluation process. ## What's Included - **Company Identity**: Name, website, industry, and HQ location - **Cloud Topics**: Research categories driving the signal (AWS migration, Azure Active Directory, GCP data platform, hybrid cloud, Kubernetes, FinOps, etc.) - **Platform Signals**: Indicated cloud platform preference based on research topic weighting (AWS, Azure, or GCP lean) - **Signal Score and Trend**: Composite score and trend direction for research intensity - **Company Profile**: Employee count, revenue range, and current infrastructure environment signals - **Recommended Contacts**: IT Director, VP of Infrastructure, CTO, and cloud engineering contacts with verified email and phone ## Use Cases ### Cloud Migration and MSP Services Companies with strong cloud migration intent are often in the planning stage — evaluating MSPs, migration tooling, and professional services partners before committing to a timeline. This is the ideal moment for MSPs to establish relationships. Filter by company size and on-premise infrastructure signals to prioritize companies with legacy environments most likely to need migration support. ### Cloud Platform and IaaS Sales AWS, Azure, and GCP resellers and ISVs can use platform-specific signals to identify companies leaning toward their ecosystem. Companies researching Azure-specific topics (Azure AD, Microsoft 365 integration, Azure DevOps) are different buyers than those researching AWS-native architectures — this audience lets you segment accordingly. ### FinOps and Cloud Cost Optimization Companies that have already migrated often develop cloud cost management intent signals as they encounter unexpectedly high bills. FinOps platforms, cost optimization tools, and cloud financial management services should look for companies with elevated cost-related cloud signals alongside existing cloud infrastructure job postings — a strong indicator of runaway spend. ### Cloud Architecture Consulting Systems integrators and cloud architecture consulting firms can use this audience to identify companies in the early stages of cloud strategy development. Organizations researching hybrid cloud design, landing zone architecture, and cloud governance frameworks are pre-vendor decisions — the right moment for advisory engagements before platform selection locks in. ================================================================================ # Companies Showing CRM and Sales Tech Buying Intent > 4,000+ companies actively researching CRM platforms and sales technology — real-time intent signals across Salesforce, HubSpot, and adjacent sales tools **Source:** https://gtm.ai/marketplace/companies-showing-crm-intent --- ## Overview CRM and sales technology intent signals reflect companies actively evaluating their revenue stack — whether migrating off legacy CRMs, expanding their current platform, or adding adjacent tools like sales engagement, revenue intelligence, or forecasting. This audience covers 4,000+ companies with elevated research activity across Salesforce, HubSpot, Microsoft Dynamics, and the broader sales tech ecosystem. Current CRM environment signals help identify displacement versus expansion opportunities. ## What's Included - **Company Identity**: Name, website, industry, and HQ location - **Research Topics**: CRM categories driving intent (Salesforce migration, HubSpot setup, sales engagement, revenue forecasting, CPQ, contract management, etc.) - **Signal Score and Date**: Research intensity and recency of activity - **CRM Environment Signals**: Indicated current CRM based on job postings, technographic data, and content signals - **Company Profile**: Employee count, revenue range, and sales team size estimates - **Recommended Contacts**: VP of Sales, Sales Ops, RevOps, and CTO contacts with verified email and phone ## Use Cases ### CRM Platform Sales and Displacement Companies researching alternative CRMs are often unhappy with their current platform — triggered by cost increases, missed enterprise features, or a new sales leader resetting the stack. Use current CRM environment signals to identify Salesforce accounts likely to evaluate HubSpot, or legacy Dynamics shops considering a full platform replacement. Filter by company size to align with your platform's target segment. ### Sales Engagement and Enablement Tool Sales Sales engagement platforms, conversation intelligence tools, and sales enablement systems are frequently evaluated alongside CRM decisions. Companies researching CRM platforms are simultaneously evaluating the full tech stack — making this audience a strong signal for sequence tools, call recording platforms, and playbook software vendors. ### RevOps Platform and Data Sales Revenue operations platforms, data enrichment vendors, and pipeline analytics tools are natural fits for companies building or rebuilding their CRM foundation. Companies in the CRM evaluation stage are also evaluating the data and operations layer — contact enrichment, lead routing, territory management, and attribution. Target RevOps leads and VP of Sales Ops contacts in this audience. ### CRM Implementation and Consulting Salesforce partners, HubSpot agency partners, and independent RevOps consultants can use this audience to identify companies at the beginning of a CRM implementation or migration project. Companies researching CRM platforms without existing implementation partners are prime targets for consulting engagements — reach them before the platform decision locks in the implementation choice. ================================================================================ # Companies Showing Cybersecurity Buying Intent > 7,500+ companies actively researching cybersecurity solutions — real-time intent signals across endpoint, network, cloud, and identity security topics **Source:** https://gtm.ai/marketplace/companies-showing-cybersecurity-intent --- ## Overview This audience captures companies with active research signals across cybersecurity topics — endpoint detection, network security, cloud security posture, identity and access management, and threat intelligence. Intent signals are aggregated from content consumption, vendor review activity, and job posting patterns. Spikes in security intent often correspond to post-incident reviews, compliance audit cycles, or active vendor evaluations — making timing of outreach critical. ## What's Included - **Company Identity**: Name, website, industry, and HQ location - **Security Topics**: Specific research categories driving the signal (endpoint, network, SIEM, IAM, zero trust, cloud security, etc.) - **Signal Score and Spikes**: Composite intensity score and spike indicator flags for sudden increases in research activity - **Signal Date**: Date of most recent signal and historical trend - **Company Profile**: Employee count, revenue range, and regulated industry flags (financial services, healthcare, government) - **Recommended Contacts**: CISO, VP of Security, IT Director, and security engineering contacts with verified email and phone ## Use Cases ### Security Platform and Tooling Sales Companies with elevated cybersecurity intent signals are actively evaluating security tools — often triggered by a compliance audit, board mandate, or security incident. Prioritize outreach to companies with rising or spike-flagged signals, and align your messaging to the specific security topics driving their research activity. ### MSSP and Managed Detection Outreach Mid-market companies frequently research security solutions without the internal expertise to implement them — creating a natural entry point for MSSPs. Filter this audience by company size (200–2,000 employees) and regulated industries to find companies that are most likely to outsource security operations rather than build in-house. ### Zero Trust and Identity Platform Sales Identity and access management is one of the most consistently researched security topics. Use this audience to find companies that are actively evaluating zero trust architectures, SSO, MFA, and PAM solutions. Cross-reference with job postings for identity engineering roles to identify companies building internal IAM programs. ### Security Consulting and Incident Response Security consultants and incident response firms can use intent signals as an early-warning system — companies that spike on threat intelligence and forensics topics may be in the middle of an incident or conducting a post-incident review. Fast outreach to these signals can establish consulting relationships at the moment of highest urgency. ================================================================================ # Companies Showing HR and Workforce Buying Intent > 6,100+ companies actively researching HR software, HRIS, and workforce management solutions — real-time intent signals updated weekly **Source:** https://gtm.ai/marketplace/companies-showing-hr-intent --- ## Overview HR and workforce intent signals reflect companies evaluating their people operations technology stack — HRIS platforms, payroll systems, benefits administration, workforce scheduling, and talent acquisition tooling. This audience covers 6,100+ companies with elevated research activity across major HR platforms and adjacent workforce management categories. Signal intensity is particularly useful for timing outreach — spikes often correspond to annual renewal cycles, headcount growth events, or CHRO transitions. ## What's Included - **Company Identity**: Name, website, industry, and HQ location - **HR Research Topics**: Categories driving intent (HRIS selection, payroll migration, ATS evaluation, benefits administration, workforce scheduling, people analytics, etc.) - **Signal Score and Intensity**: Composite research intensity and spike indicators - **Company Profile**: Employee count, revenue range, and industry classification - **Industry Context**: Regulated industries (healthcare, financial services, government) and high-turnover industries (retail, hospitality, logistics) flagged separately - **Recommended Contacts**: CHRO, VP of People, HR Director, and People Ops Manager contacts with verified email and phone ## Use Cases ### HRIS and HR Platform Sales Companies researching HRIS platforms are often migrating off legacy systems like ADP Workforce Now, Paychex, or older on-premise HR software. Use signal topics to identify whether a company is evaluating core HRIS functionality, performance management, or a full HCM suite. Employee count is a reliable filter for platform fit — sub-500 employees typically evaluates SMB platforms; 500+ evaluates enterprise HCM. ### Payroll and Benefits Solution Sales Payroll and benefits intent signals frequently co-occur with HRIS research — companies migrating HR platforms often rebid payroll and benefits at the same time. Payroll software vendors and benefits brokers should filter for multi-topic signals to identify companies in a full HR stack evaluation rather than a single point solution purchase. ### Workforce Management and Scheduling Tools Workforce scheduling intent is heavily concentrated in retail, healthcare, manufacturing, and logistics — industries where shift-based scheduling is operationally critical. Filter this audience by industry to find companies actively evaluating scheduling software, time and attendance systems, and labor cost optimization tools. Operations and HR Director contacts are the primary buyers in these segments. ### HR Consulting and Advisory HR consulting firms and fractional HR services can use this audience to identify companies that are building out their people operations function — often triggered by rapid headcount growth, a new CHRO hire, or post-merger integration. Companies researching broad HR platform categories without a clear incumbent are often in the advisory stage before platform selection. ================================================================================ # Companies Using Atlassian (Jira / Confluence) > 137,000+ companies with confirmed Atlassian product installations — engineering and product organizations running Jira, Confluence, or other Atlassian tools as their core work management platform. **Source:** https://gtm.ai/marketplace/companies-using-atlassian --- ## Overview Atlassian's Jira and Confluence are the backbone of engineering and product workflows at tens of thousands of companies globally. Organizations running Atlassian have made a meaningful investment in structured project tracking and documentation, making them active buyers of DevOps tools, productivity integrations, and engineering management software that connects to their Atlassian environment. ## What's Included Confirmed Atlassian product installation data and company firmographics. The audience spans software companies, agencies, and enterprises across all sizes that rely on Jira or Confluence as their core engineering and product management toolchain. ## Use Cases - **Atlassian Marketplace vendors** targeting their exact addressable market of active Atlassian users - **DevOps and CI/CD tools** selling to engineering organizations centered on Jira workflows - **Work management competitors** (Linear, Shortcut, Notion) building displacement campaigns against Atlassian ================================================================================ # Companies Using Databricks > 15,900+ companies with confirmed Databricks installations — a high-signal audience for data engineering, AI/ML, and lakehouse ecosystem vendors. **Source:** https://gtm.ai/marketplace/databricks-users --- ## Overview Databricks users are among the most technically sophisticated data organizations in the market — they're running large-scale data engineering, machine learning, and AI workflows on a unified lakehouse platform. This audience of 15,900+ confirmed Databricks installations represents the core buyer segment for AI/ML tools, data pipeline vendors, and cloud infrastructure providers. ## What's Included Records include confirmed Databricks installation data alongside company firmographics. The audience skews toward larger, data-mature organizations in technology, financial services, healthcare, and life sciences. ## Use Cases - **AI/ML vendors** (feature stores, model serving, MLOps) targeting companies running active ML workloads - **Data engineering tools** selling to engineering-led data organizations - **Cloud infrastructure providers** competing for or complementing Databricks deployments ================================================================================ # Companies Using Figma > 46,100+ companies with confirmed Figma installations — design-forward organizations that have adopted collaborative design as a core part of their product development workflow. **Source:** https://gtm.ai/marketplace/companies-using-figma --- ## Overview Figma has become the industry-standard collaborative design tool — and the companies running it have invested in design-led product development. These 46,100+ organizations have design and product teams working in a shared visual environment, making them ideal targets for UX research tools, design systems platforms, and developer handoff solutions that plug into Figma workflows. ## What's Included Confirmed Figma installation data alongside company firmographics. The audience is concentrated among software companies, agencies, and technology-forward brands where product design plays a central role. ## Use Cases - **UX research and usability testing platforms** (UserTesting, Maze) selling to companies with active Figma design workflows - **Design-to-code and developer handoff tools** targeting organizations using Figma as the source of truth for UI - **Design system and token management platforms** reaching companies scaling their design infrastructure ================================================================================ # Companies Using Gong > 575 companies with confirmed Gong revenue intelligence installations — a highly selective audience of sales-driven organizations running the leading conversation intelligence platform. **Source:** https://gtm.ai/marketplace/gong-users --- ## Overview Companies running Gong have made a deliberate investment in revenue intelligence and conversation analytics — a strong signal that they have mature, data-driven sales organizations. While the installed base is smaller than broad CRM platforms, these companies are among the most sophisticated sales-led businesses in the market, making this a precision audience for complementary sales tech. ## What's Included Confirmed Gong installation data and company firmographics. Companies in this audience tend to run modern sales stacks with investment in CRM, engagement, and forecasting tools alongside Gong. ## Use Cases - **Sales enablement and coaching vendors** targeting companies already invested in call intelligence - **CRM and forecasting platforms** selling into organizations with mature revenue operations - **Sales compensation and quota management tools** targeting data-driven sales organizations ================================================================================ # Companies Using HubSpot > 428,000+ companies with confirmed HubSpot installations — ideal for partners, competitors, and vendors selling into the HubSpot ecosystem. **Source:** https://gtm.ai/marketplace/hubspot-users --- ## Overview HubSpot has become the dominant CRM and marketing automation platform for SMB and mid-market companies. With 428,000+ confirmed installations globally, this audience represents the broadest technographic signal for vendors selling into the HubSpot ecosystem — from native integrations to complementary tools to competitive alternatives. ## What's Included Records include company firmographics and confirmed HubSpot installation data, verified through ZoomInfo's continuous technographic detection. Companies range from small businesses to mid-market firms, with heavy concentration in North America and Europe. ## Use Cases - **HubSpot ecosystem vendors** (integrations, data enrichment, RevOps tools) prospecting their total addressable market - **Competitive CRM and MAP vendors** building displacement campaigns against HubSpot accounts - **HubSpot agencies and consultants** finding companies that need optimization or migration support ================================================================================ # Companies Using Intercom > 52,900+ companies with confirmed Intercom installations — growth-stage and mid-market businesses running the leading customer messaging and support platform. **Source:** https://gtm.ai/marketplace/companies-using-intercom --- ## Overview Intercom is the dominant customer messaging platform for product-led and growth-stage companies — it sits at the intersection of marketing, support, and product engagement. Companies running Intercom have invested in real-time customer communication and are active buyers of complementary tools: AI deflection, CRM integrations, and advanced analytics on customer conversations. ## What's Included Confirmed Intercom installation data paired with company firmographics. The audience skews toward SaaS, e-commerce, and consumer tech companies in the 10–500 employee range, where Intercom's product-led model resonates most. ## Use Cases - **AI support automation vendors** targeting companies with existing chat infrastructure to layer intelligence on top - **CRM and data sync vendors** selling Intercom-to-CRM integrations to companies managing customer data across tools - **Competitive messaging platforms** building displacement campaigns against Intercom accounts at scale ================================================================================ # Companies Using Marketo > 31,200+ companies with confirmed Adobe Marketo Engage installations — the definitive audience for marketing automation vendors, Marketo integrators, and enterprise marketing tech. **Source:** https://gtm.ai/marketplace/marketo-users --- ## Overview Adobe Marketo Engage is the enterprise marketing automation platform of choice for B2B organizations with complex multi-channel demand generation needs. Companies running Marketo typically pair it with Salesforce CRM, making this audience highly relevant for both MAP ecosystem vendors and the broader Salesforce partner network. ## What's Included Confirmed Marketo installation data and company firmographics. The audience is concentrated among mid-market and enterprise B2B companies, particularly in technology, professional services, and financial services. ## Use Cases - **MarTech integration vendors** (data enrichment, ABM, attribution) selling into the Marketo ecosystem - **Competitive MAP platforms** building displacement campaigns against Marketo accounts - **RevOps and demand gen consultants** targeting companies running Marketo-Salesforce stacks ================================================================================ # Companies Using Outreach > 1,400+ companies with confirmed Outreach sales engagement platform installations — a precise audience of outbound-focused sales organizations running modern engagement infrastructure. **Source:** https://gtm.ai/marketplace/outreach-users --- ## Overview Companies running Outreach have invested in systematic, sequence-driven sales engagement — they understand outbound at scale and are active buyers of data, prospecting, and conversion tools. With 1,400+ confirmed installations, this is a precision audience of modern outbound-oriented sales organizations. ## What's Included Confirmed Outreach installation data and company firmographics. The audience is concentrated among mid-market B2B technology and professional services companies with established sales development and outbound motion. ## Use Cases - **Prospecting data vendors** targeting companies running high-velocity outbound sequences - **Competitive sales engagement platforms** identifying Outreach displacement opportunities - **Sales operations and RevOps tools** targeting teams already running structured engagement workflows ================================================================================ # Companies Using Salesforce CRM > 149,000+ companies with confirmed Salesforce CRM installations — the definitive audience for Salesforce ecosystem vendors, consultants, and competitive displacement plays. **Source:** https://gtm.ai/marketplace/salesforce-crm-users --- ## Overview Salesforce is the world's most widely deployed CRM, and companies using it share a common technology context: they've invested in Salesforce workflows, data models, and integrations. This audience of 149,000+ confirmed Salesforce installations is essential for any vendor selling into the Salesforce ecosystem — from AppExchange partners to consultants to complementary tools. ## What's Included Each record is a company with a verified Salesforce CRM installation detected through ZoomInfo's technographic data collection. Company firmographics — size, industry, location, revenue — are included to support segmentation and prioritization. ## Use Cases - **Salesforce ISVs and AppExchange vendors** targeting their exact addressable market - **CRM migration vendors** building displacement pipelines against Salesforce accounts - **Salesforce implementation and consulting partners** prospecting companies in need of optimization ================================================================================ # Companies Using Segment CDP > 41,300+ companies with confirmed Segment Customer Data Platform installations — data-driven organizations that have centralized their customer event data collection and routing. **Source:** https://gtm.ai/marketplace/companies-using-segment --- ## Overview Segment users have made a deliberate investment in customer data infrastructure — they're collecting structured event data, routing it to downstream tools, and building a foundation for personalization and analytics. Companies running Segment are among the most data-mature buyers in the market and are active evaluators of the downstream tools that consume and activate their customer data. ## What's Included Confirmed Segment CDP installation data and company firmographics. The audience is concentrated among SaaS, fintech, and e-commerce companies with active product analytics and customer data programs. ## Use Cases - **Data activation and reverse-ETL vendors** targeting companies with structured Segment event streams - **Personalization and experimentation platforms** selling to companies with Segment as their data foundation - **Competing CDP vendors** (RudderStack, mParticle) building displacement campaigns against Segment ================================================================================ # Companies Using ServiceNow > 28,900+ companies with confirmed ServiceNow installations — ideal for IT service management vendors, enterprise workflow tools, and ServiceNow ecosystem partners. **Source:** https://gtm.ai/marketplace/servicenow-users --- ## Overview ServiceNow is the dominant enterprise IT service management and workflow platform, with installations concentrated in mid-to-large enterprises across technology, financial services, healthcare, and government. Companies running ServiceNow are active buyers of IT operations, security, and workflow tools — and represent a mature, well-funded segment of the enterprise technology market. ## What's Included Confirmed ServiceNow installation data alongside company firmographics. The audience skews heavily toward larger enterprises with 1,000+ employees, reflecting ServiceNow's typical deployment profile. ## Use Cases - **ITSM and IT operations vendors** building integrations or competitive displacement campaigns - **Enterprise AI vendors** targeting companies modernizing ServiceNow workflows - **ServiceNow implementation and consulting partners** prospecting expansion and optimization opportunities ================================================================================ # Companies Using Slack > 28,800+ companies with confirmed Slack installations — organizations that have adopted real-time team messaging as their primary internal communication layer. **Source:** https://gtm.ai/marketplace/companies-using-slack --- ## Overview Slack has become the default communication layer for tech-forward companies — and with that comes an entire ecosystem of bots, integrations, and workflow tools built on top of it. Companies running Slack are signal-rich buyers for productivity, automation, and employee experience tools that plug into their existing messaging infrastructure. ## What's Included Confirmed Slack installation data and company firmographics. The audience is concentrated among technology, media, and knowledge-work companies that have standardized on Slack as their primary asynchronous and real-time communication tool. ## Use Cases - **Slack app and workflow automation vendors** prospecting their addressable Slack-native market - **Employee experience and HR tools** selling to companies using Slack as an engagement layer - **Microsoft Teams vendors** building displacement campaigns against Slack-standard organizations ================================================================================ # Companies Using Snowflake > 18,500+ companies with confirmed Snowflake Data Cloud installations — the core audience for data tooling vendors, Snowflake partners, and data infrastructure providers. **Source:** https://gtm.ai/marketplace/snowflake-data-cloud-users --- ## Overview Companies running Snowflake have made a significant investment in cloud data infrastructure and are typically active buyers of the broader modern data stack — ETL/ELT tools, transformation layers, BI platforms, data observability, and governance solutions. With 18,500+ confirmed installations, this audience defines the Snowflake partner ecosystem's addressable market. ## What's Included Each company record includes confirmed Snowflake installation status, firmographic data, and location. Companies span technology, financial services, healthcare, and retail — any vertical that has modernized its data infrastructure. ## Use Cases - **Data stack vendors** (dbt, Fivetran, Airbyte, Sigma, Looker) selling to companies already on Snowflake - **Security and governance platforms** targeting Snowflake data environments - **Snowflake implementation consultants** finding companies that need migration or optimization ================================================================================ # Companies Using Stripe > 545,000+ companies with confirmed Stripe payment infrastructure — businesses that have adopted developer-first payment processing as the foundation of their monetization stack. **Source:** https://gtm.ai/marketplace/companies-using-stripe --- ## Overview Stripe has become the default payment infrastructure for internet businesses — from solo developers to enterprise SaaS. With 545,000+ confirmed Stripe installations, this audience covers a defining cross-section of the modern digital economy: SaaS companies, marketplaces, e-commerce brands, and any business collecting revenue online. It's the foundational signal for fintech ecosystem vendors and revenue operations tools. ## What's Included Confirmed Stripe installation data alongside company firmographics. The audience spans all company sizes and geographies, with heavy concentration in North America and Europe across software, e-commerce, and marketplace verticals. ## Use Cases - **Revenue recognition and billing platforms** selling to companies managing complex Stripe subscription data - **Fraud detection and payment analytics** vendors targeting companies processing significant Stripe volume - **Financial operations tools** (accounting, reconciliation, tax) selling to businesses built on Stripe's payment rails ================================================================================ # Companies Using Workday > 28,700+ companies with confirmed Workday installations — the go-to audience for HR tech, payroll, workforce analytics, and financial management vendors in the Workday ecosystem. **Source:** https://gtm.ai/marketplace/workday-users --- ## Overview Workday is the enterprise HR and financial management platform of choice for mid-to-large organizations. Companies running Workday have made a significant investment in unified HR and finance infrastructure and represent active buyers for complementary workforce, benefits, and analytics tools. With 28,700+ confirmed installations, this audience is foundational for any HR tech or enterprise software vendor. ## What's Included Confirmed Workday installation data paired with company firmographics. The audience skews toward mid-market and enterprise companies with 250+ employees — organizations mature enough to be running enterprise-grade HR systems. ## Use Cases - **HR tech vendors** (benefits, engagement, L&D) selling into Workday-integrated HR stacks - **Workforce analytics platforms** targeting organizations with structured HR data - **Workday implementation partners** prospecting companies that need services and optimization ================================================================================ # Companies Using Zendesk > 170,000+ companies with confirmed Zendesk installations — essential for customer experience vendors, CX integrators, and competitive customer support platforms. **Source:** https://gtm.ai/marketplace/zendesk-users --- ## Overview Zendesk is one of the most widely deployed customer support platforms globally, with installations concentrated in SaaS, e-commerce, financial services, and technology companies. This audience of 170,000+ confirmed Zendesk users is the definitive starting point for any vendor operating in the customer experience and support space. ## What's Included Each record includes confirmed Zendesk installation data and company firmographics. The audience spans SMBs through mid-market companies, with strong representation across consumer-facing and B2B SaaS businesses. ## Use Cases - **AI support vendors** (chatbots, auto-resolution, ticket deflection) targeting existing Zendesk infrastructure - **CX platform competitors** building displacement pipelines against Zendesk accounts - **Zendesk integration partners** prospecting their exact addressable market ================================================================================ # Companies with 1,000 to 5,000 Employees > 63,000+ companies in the 1,000–5,000 employee range — upper mid-market with complex buying committees and multi-year software budgets **Source:** https://gtm.ai/marketplace/companies-1000-to-5000-employees --- ## Overview The 1,000–5,000 employee range defines the upper mid-market — organizations with dedicated IT, finance, HR, and legal functions, multi-year software budgets, and buying committees that typically include 5–10 stakeholders. This audience covers 63,000+ companies globally in this band, enriched with department-level headcount, tech stack signals across business functions, org structure data, and verified VP and C-suite contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and global headquarters - **Headcount Profile**: Total employee count with department-level breakdown and 1-year growth rate - **Financials**: Revenue estimates and funding stage (for private companies) - **Tech Stack**: Detected software tools across CRM, ERP, HCM, IT, security, and marketing - **Org Structure**: Subsidiary and division mapping with known reporting hierarchies - **Executive Contacts**: Verified email and phone for C-suite and VP-level contacts across all major functions ## Use Cases ### Upper Mid-Market Enterprise Sales This segment requires multi-threaded account strategies — multiple stakeholders, longer sales cycles, and more formal procurement processes. Use department headcount and tech stack to build the full account map before the first outreach. ### Multi-Stakeholder Deal Navigation Buying committees in this segment span IT, finance, operations, and the business unit. Org structure data and VP-level contacts allow reps to identify the economic buyer, the champion, and the technical evaluator before the first discovery call. ### HR and Workforce Technology At 1,000–5,000 employees, HR teams are managing enterprise-scale workforce complexity — performance management, compliance, benefits, and talent development. Use department headcount and current HR tech stack to identify which companies are on legacy systems and overdue for an upgrade. ### IT Infrastructure and Security Organizations in this size range are managing multi-site IT infrastructure, hybrid cloud environments, and growing security surface areas. Tech stack detection identifies which security and infrastructure categories are covered and which are gaps — enabling precise, needs-based outreach. ================================================================================ # Companies with 500 to 1,000 Employees > 138,000+ companies in the 500–1,000 employee range — established enough to have real budget, small enough to move fast **Source:** https://gtm.ai/marketplace/companies-500-to-1000-employees --- ## Overview The 500–1,000 employee band is the sweet spot for many enterprise software vendors — companies large enough to have formal procurement, dedicated department heads, and real software budgets, but small enough that a motivated champion can move a deal through the organization in a single quarter. This globally scoped audience covers 138,000+ companies in this range, segmented by industry, geography, growth rate, and revenue estimates. ## What's Included - **Company Identity**: Verified name, primary domain, and headquarters location - **Headcount**: Verified employee count and 1-year growth rate with department breakdown - **Revenue Estimates**: Model-derived revenue ranges based on industry, headcount, and market data - **Industry Classification**: NAICS/SIC-based industry and sub-industry tagging - **Office Footprint**: HQ location and known satellite office count - **Decision-Maker Contacts**: Verified email and phone for VP and director-level contacts across relevant functions ## Use Cases ### Mid-Market Sales Targeting This band represents the core mid-market — the segment where most B2B software companies find the best unit economics. Use industry, geography, and growth rate to build precise ICP segments and prioritize accounts with the highest propensity to buy. ### Channel Partner Recruitment Companies in this size range often make excellent channel partners — they have the market presence and customer relationships to drive meaningful co-sell volume without the bureaucracy of larger enterprise partners. Filter by industry and geography to find regional champions. ### HR and Workforce Solutions At 500–1,000 employees, companies are formalizing HR operations, evaluating HRIS replacements, and investing in benefits administration and talent management. Use headcount growth rate to identify which companies are outgrowing their current people infrastructure. ### Operations and Infrastructure Sales ERP, IT infrastructure, cybersecurity, and facilities management vendors find this segment particularly productive — these companies have outgrown SMB solutions but haven't yet standardized on enterprise platforms, creating strong displacement opportunities. ================================================================================ # Compliance Officers at Financial Institutions > 40,900+ Chief Compliance Officers and compliance leaders at banks, insurance companies, and financial services firms **Source:** https://gtm.ai/marketplace/compliance-officers-financial --- ## Overview This audience targets Chief Compliance Officers, Deputy CCOs, and VP-level compliance leaders at banks, credit unions, insurance carriers, asset managers, broker-dealers, and fintech companies globally. Each contact is enriched with regulatory framework context — identifying whether the institution operates under SOX, BSA/AML, FINRA, FCA, MiFID II, or other applicable regimes — enabling precise segmentation by compliance burden and regulatory risk profile. This is the primary buyer persona for RegTech, GRC platforms, AML/KYC tools, and compliance consulting services. ## What's Included - **Identity**: Full name, verified email, direct phone, and LinkedIn profile URL - **Role Context**: Current title and regulatory focus area (AML, BSA, sanctions, operational risk, consumer protection) - **Institution Context**: Institution type (bank, insurance, asset manager, broker-dealer, fintech) and applicable regulatory frameworks - **Institution Size**: AUM or asset size estimates, employee count - **Team Context**: Compliance team structure and estimated headcount ## Use Cases ### RegTech and Compliance Platform Sales GRC platforms, policy management tools, regulatory change management software, and compliance workflow automation vendors target CCOs and VP-level compliance leaders as the primary economic buyer. Regulatory framework filters let you segment by applicable regime — GDPR compliance buyers have different requirements than BSA/AML buyers. ### AML and KYC Solution Sales Anti-money laundering, know-your-customer, and transaction monitoring platforms sell primarily into compliance and operations teams at banks, money services businesses, and fintech companies. Institution type and asset size attributes help prioritize accounts by deal complexity and regulatory exposure. ### Legal and Regulatory Consulting Law firms and boutique compliance consultancies targeting financial institutions can identify CCOs and compliance leaders at firms facing regulatory examination cycles, enforcement actions, or upcoming compliance deadlines driven by new rules or framework changes. ### Audit and Risk Management Services Internal audit firms and risk consulting practices targeting financial services compliance functions can use institution size and regulatory framework data to focus on accounts where compliance complexity justifies external support. ================================================================================ # Construction Technology Companies > 108,000+ construction and built environment technology companies with employee count, funding data, and decision-maker contacts **Source:** https://gtm.ai/marketplace/construction-technology-companies --- ## Overview Construction technology is undergoing rapid transformation — from project management software and BIM platforms to robotics, prefabrication, and IoT-enabled job site monitoring. This audience covers 108,000+ construction and built environment technology companies globally, spanning general contractors, specialty subcontractors, construction software vendors, and materials technology firms, with sub-category classification and verified decision-maker contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and headquarters location - **Sub-Category**: Project management software, BIM/design tools, job site technology, materials tech, or construction services - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Project Focus**: Residential, commercial, industrial, or infrastructure project specialization - **Executive Contacts**: Verified email and phone for CEO, CTO, VP of Operations, and VP of Sales ## Use Cases ### ConTech Platform Sales Data, analytics, and workflow software vendors selling into construction should use project focus and sub-category to align their solution to the right buyer. A commercial GC managing multi-site projects has different software requirements than a residential homebuilder. ### Building Materials and Equipment Sales Manufacturers and distributors of building materials, tools, and equipment can use this audience to identify construction technology companies driving demand for their products — particularly firms implementing new construction methods that require specialized materials. ### Real Estate Developer Outreach Real estate developers and owners are the end customers for many construction technology vendors. Use project focus and geography to identify ConTech companies whose technology addresses specific development challenges relevant to your portfolio or target markets. ### Investor Deal Sourcing in Construction Tech Construction tech investors can filter by sub-category, funding stage, and headcount growth to identify companies gaining traction in high-priority segments. Project focus classification helps distinguish horizontal platforms from vertical specialists — an important distinction for assessing market size and competitive dynamics. ================================================================================ # CTOs at Enterprise Companies > 12,000+ Chief Technology Officers at companies with $100M+ revenue — verified contacts, technology environment, and engineering org context **Source:** https://gtm.ai/marketplace/ctos-at-enterprise-companies --- ## Overview Enterprise CTOs are the primary buyers and influencers for infrastructure, developer tooling, cloud platforms, and data engineering technology. This audience covers 12,000+ CTOs at companies with $100M+ revenue — a threshold that ensures alignment with organizations that have dedicated engineering organizations, significant technology budgets, and multi-year infrastructure roadmaps. Records include tech stack context, cloud platform signals, and engineering org size to enable precise targeting. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **Title and Tenure**: Full CTO title, time in role at current company, and management level context - **Company Profile**: Revenue tier ($100M–$500M, $500M–$1B, $1B+), employee count, and industry classification - **Technology Environment**: Known cloud platforms (AWS, Azure, GCP), primary programming languages, and key infrastructure tools from technographic signals - **Engineering Org Size**: Engineering headcount estimates as a proxy for infrastructure scale and tooling complexity - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Enterprise Infrastructure and Platform Sales Database vendors, observability platforms, data engineering tools, and API infrastructure providers target enterprise CTOs as the primary economic buyer. Use technology environment signals to identify companies running on a specific cloud platform or using specific languages and frameworks — and position your platform as the natural fit for their existing stack. Engineering org size helps calibrate deal size expectations and implementation complexity. ### Cloud and DevOps Tool Sales CI/CD platforms, container orchestration tools, infrastructure-as-code solutions, and developer experience platforms target CTOs and VP of Engineering contacts at enterprise companies. Enterprise CTOs are evaluating build vs. buy decisions across their toolchain — outreach that leads with developer productivity benchmarks and integration depth with their existing stack (GitHub, Kubernetes, Terraform) outperforms generic cloud sales messaging. ### Technology Consulting and Advisory System integrators, technology strategy consultants, and architecture advisory firms target enterprise CTOs who are navigating technology modernization, cloud migration, and digital transformation programs. CTOs at $100M+ companies have board-level mandates to reduce technical debt and modernize infrastructure — but need external expertise and delivery capacity to execute. Filter by industry to align with your firm's sector-specific technology experience. ### Engineering Leadership Recruiting Executive search firms and in-house talent teams targeting CTO and VP of Engineering hires use this audience to map the engineering leadership landscape, identify passive candidates, and track talent flows between companies. Tenure data helps identify CTOs who may be approaching a natural career transition point — useful for both recruiting and competitive intelligence programs. ================================================================================ # CTOs at Series A and B Startups > 74,900+ Chief Technology Officers at Series A and B funded companies — verified contacts, tech stack, and engineering team context **Source:** https://gtm.ai/marketplace/ctos-at-series-ab-startups --- ## Overview This audience covers 74,900+ CTOs at Series A and B funded companies — the cohort where technical infrastructure decisions are being made for the first time at scale. Series A and B CTOs are actively selecting cloud platforms, developer tooling, and data infrastructure rather than inheriting legacy choices, making this audience high-value for early sales motion and category creation. Engineering team size and tech stack data reduce discovery time and enable more precise, relevant outreach. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, tenure, and founding team status where applicable - **Funding Data**: Series A or B classification with most recent round size and date - **Engineering Team**: Estimated engineering headcount and growth trajectory - **Tech Stack**: Known infrastructure, cloud, and development tooling in use ## Use Cases ### Developer Tooling and Infrastructure Sales Target CTOs at Series A and B startups before their tooling decisions solidify. This cohort is actively evaluating CI/CD pipelines, observability platforms, and developer experience tooling — and the CTO is typically the direct buyer at this company stage. ### Cloud and DevOps Platform Sales Reach CTOs making cloud platform commitments as their engineering teams scale. Series B companies are often at the inflection point where infrastructure standardization becomes a priority, creating a receptive audience for cloud management and DevOps tools. ### Engineering Recruiting and Staffing Source technical leaders at funded startups building out engineering teams. Series A and B CTOs are consistently among the most active hiring managers in the market, and direct contact data accelerates recruiter outreach past the LinkedIn InMail queue. ### Technical Advisory and Consulting Services Identify CTOs at growth-stage startups who lack the organizational depth to handle architecture reviews, security audits, or vendor evaluations internally. Funding recency signals companies with budget for outside technical expertise. ================================================================================ # Customer Experience and Support Leaders > VP Customer Experience, Head of CX, Director of Support, and VP Support leaders — the buyers responsible for the tools and teams delivering post-sale customer experience. **Source:** https://gtm.ai/marketplace/customer-experience-leaders --- ## Overview Customer Experience and Support leaders are responsible for one of the most tool-dependent functions in business — they manage support ticketing systems, AI chatbots, workforce management, quality assurance, and voice of customer programs. With 22,000+ verified CX and support leaders, this audience captures the buyers who are actively evaluating and managing a rich ecosystem of support technology. ## What's Included Verified contacts with VP Customer Experience, Head of CX, VP Support, Director of Customer Support, or Head of Support titles. Direct email and phone with company firmographics. ## Use Cases - **Customer support and helpdesk platforms** reaching the leaders who own the tooling decision - **AI support automation vendors** targeting CX leaders looking to reduce ticket volume and improve CSAT - **Workforce management tools** selling to support leaders managing large, distributed agent teams ================================================================================ # Customer Success Leaders in B2B SaaS > 5,700+ Director, VP, and C-suite Customer Success leaders at B2B SaaS companies — verified contacts, company ARR range, and CS tech stack **Source:** https://gtm.ai/marketplace/customer-success-leaders-saas --- ## Overview Customer Success leaders at B2B SaaS companies are the primary buyers of CS platforms, health scoring tools, digital CS infrastructure, and customer data systems. This audience covers 5,700+ Director, VP, and CCO-level CS leaders globally — with ARR range, CS tech stack context, and expansion revenue signals to help prioritize the highest-fit targets. As SaaS companies shift focus from new logo acquisition to net revenue retention, CS leadership has emerged as a primary budget owner across the revenue tech stack. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **Title and Org Scope**: Full title (VP of Customer Success, Director of CS, Chief Customer Officer, VP of Customer Experience) and estimated CS team size - **Company ARR Context**: ARR range estimates and growth stage classification - **CS Tech Stack**: Known customer success platform, health scoring tools, and CRM integrations where available from technographic data - **Retention Signals**: Customer count estimates and expansion revenue signals where available - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Customer Success Platform Sales CS platform vendors — Gainsight, Totango, ChurnZero, and their competitors — target VP and Director of CS contacts at companies that have outgrown spreadsheet-based CS workflows. Use ARR range and CS team size to identify companies at the inflection point where a purpose-built CS platform becomes economically justified. Companies with 50+ customers and a CS team of 3+ are typically at this threshold. ### Churn Prediction and Health Scoring Tools Health scoring and churn prediction tools are often the first CS-specific investment at early-stage SaaS companies, before they invest in a full CS platform. Target Director-level CS contacts at Series A and B companies where churn is becoming a measurable problem but the organization isn't ready for an enterprise CS platform. The CS tech stack field helps identify companies without an existing health scoring solution. ### CS Consulting and Advisory Customer success strategy consultants, CS operations advisors, and fractional CCO services target CS leaders who are building their function from scratch or scaling through an inflection point. CS leaders at companies in the $5M–$50M ARR range are frequently looking for external frameworks and benchmarks to structure their playbooks, hiring, and tooling decisions. ### CS Community and Peer Program Outreach CS-focused communities, peer roundtables, and professional development programs target VP and Director of CS contacts at B2B SaaS companies. Customer success is a relatively young function — CS leaders actively seek peer benchmarks, playbook sharing, and community validation. Use company ARR and stage to segment invitations to CS communities by appropriate peer cohort. ================================================================================ # Cybersecurity Companies in the US > 37,500+ US cybersecurity companies across endpoint, network, cloud, identity, and GRC with funding data and executive contacts **Source:** https://gtm.ai/marketplace/cybersecurity-companies-us --- ## Overview The US cybersecurity market is one of the most fragmented in enterprise software — dozens of sub-categories, thousands of vendors, and buyers who are simultaneously evaluating point solutions and consolidating platforms. This audience covers 37,500+ US cybersecurity companies across endpoint protection, network security, cloud security, identity and access management, GRC, threat intelligence, and security operations, with category classification and verified executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters - **Security Category**: Endpoint, network, cloud, identity/IAM, GRC, threat intelligence, SIEM/SOAR, or application security - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Go-to-Market Model**: Direct sales, channel-led, MSSP/MDR, or marketplace distribution - **Executive Contacts**: Verified email and phone for CEO, CISO, CTO, VP of Sales, and VP of Channel ## Use Cases ### Security Vendor Sales and Partnership Data platforms, AI infrastructure, and threat intelligence providers selling to security vendors can use category classification and go-to-market model to identify companies with the right technology architecture and distribution motion for a meaningful partnership. ### MSSP and Channel Development Security vendors building indirect distribution should use the go-to-market model filter to identify MSSPs, MDR providers, and VARs already operating in the security channel. Headcount and funding stage help prioritize channel partners with the resources and momentum to drive meaningful revenue. ### Investor Deal Sourcing in Security Security investors can filter by category, funding stage, and headcount growth to identify companies gaining traction in high-priority segments. Category classification is essential — endpoint and network security have very different competitive dynamics than identity or GRC, and valuations reflect it. ### Competitive Landscape Analysis Security vendors can map the full competitive landscape by category and customer segment to identify where consolidation is occurring, which point-solution categories are being absorbed into platforms, and where new entrants are gaining traction at the expense of incumbents. ================================================================================ # Data and Analytics Companies > 83,000+ data, analytics, and business intelligence companies globally with tech stack, funding data, and decision-maker contacts **Source:** https://gtm.ai/marketplace/data-analytics-companies --- ## Overview Data and analytics companies are simultaneously the most sophisticated buyers and the most demanding evaluators of data infrastructure, tooling, and platform capabilities. This audience covers 83,000+ companies globally building business intelligence, data engineering, machine learning, and data governance solutions, with category classification, detected data stack, funding data, and verified VP and C-suite contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and global headquarters - **Analytics Category**: BI/visualization, data engineering, ML/AI platforms, data governance, observability, or analytics services - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Data Stack**: Detected data infrastructure tools (Snowflake, Databricks, dbt, Fivetran, etc.) - **Executive Contacts**: Verified email and phone for CEO, CTO, Chief Data Officer, VP of Engineering, and VP of Sales ## Use Cases ### Data Platform and Tooling Sales Cloud data platform vendors, integration tools, and data quality solutions should use analytics category and detected data stack to identify companies whose infrastructure architecture is compatible with or complementary to their product. Companies already using adjacent tools in the modern data stack are the highest-propensity prospects. ### Analytics Consulting and Services Data engineering consultancies, BI implementation firms, and MLOps service providers can use category and data stack detection to identify companies that have adopted the technologies they specialize in — and are therefore most likely to need implementation, migration, or optimization services. ### Cloud Data Infrastructure Sales Compute, storage, and data transfer vendors targeting data companies can use detected data stack to identify which cloud platforms and infrastructure providers are already in use — and where there are displacement opportunities or expansion paths into adjacent workloads. ### Investor Deal Sourcing Data infrastructure and analytics investors can filter by category, funding stage, and detected data stack to identify companies gaining traction in specific segments. Data stack detection provides a proxy for technical sophistication and market adoption that funding data alone doesn't capture. ================================================================================ # Data Engineers > 261,000+ Data Engineers — the infrastructure builders managing pipelines, warehouses, and data transformation layers that power modern analytics and AI applications. **Source:** https://gtm.ai/marketplace/data-engineers --- ## Overview Data Engineers are the backbone of the modern data stack — they design and maintain the pipelines, transformations, and infrastructure that move data from source systems into warehouses, lakes, and downstream analytics and AI applications. With 261,000+ verified records, this audience captures the hands-on practitioners who evaluate, implement, and own the data tooling that drives organizational intelligence. ## What's Included Verified contacts with Data Engineer titles across all seniority levels. Direct email and phone with company firmographics for targeting by industry, company size, and data maturity. ## Use Cases - **ETL/ELT and pipeline vendors** (Fivetran, Airbyte, dbt) reaching their primary practitioner buyer - **Data observability platforms** (Monte Carlo, Great Expectations) targeting engineers responsible for pipeline reliability - **Cloud data infrastructure vendors** reaching the engineers specifying and managing warehouse and lake architecture ================================================================================ # Data Engineers at Fortune 1000 Companies > 186,000+ Data Engineers at Fortune 1000 companies — verified contacts, tech stack, and data infrastructure context **Source:** https://gtm.ai/marketplace/data-engineers-fortune-1000 --- ## Overview This audience covers 186,000+ Data Engineers at Fortune 1000 companies — the practitioners who build, operate, and extend the data infrastructure that powers analytics, ML, and operational systems at the largest US companies. Known data stack coverage across cloud data warehouses, orchestration tools, transformation frameworks, and pipeline platforms enables vendors to segment by technology ecosystem and deliver technically credible outreach. Seniority data distinguishes senior and staff engineers who influence architecture decisions from junior contributors who do not. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and tenure at current company - **Company Data**: Company name, Fortune 1000 rank range, revenue tier, and industry - **Data Stack**: Known tools across data warehouse, pipeline, orchestration, transformation, and observability categories - **Team Context**: Engineering organization size and data team structure where available ## Use Cases ### Data Platform and Infrastructure Sales Reach Data Engineers at Fortune 1000 companies who evaluate and implement core data infrastructure. Known data stack data enables platform-specific messaging — Snowflake shops, Databricks environments, and legacy on-premise warehouses each represent different migration and expansion sales motions. ### Cloud Data Warehouse and Pipeline Tools Target Data Engineers actively building or scaling cloud data pipelines and warehouse integrations. Fortune 1000 companies carry the data volume and operational complexity where premium pipeline and warehouse tooling creates measurable ROI. ### Data Observability and Governance Solutions Identify Data Engineers at large enterprises where data quality, lineage, and governance have become operational requirements — particularly in regulated industries like financial services, healthcare, and telecommunications. ### Technical Recruiting and Staffing Source experienced Data Engineers at Fortune 1000 companies for enterprise data engineering roles. Seniority and tech stack data enable recruiters to match candidates to specific open roles without relying entirely on LinkedIn sourcing. ================================================================================ # Data Scientists > 202,000+ Data Scientists across industries — the analytical practitioners driving data-driven decision-making and a core persona for data tooling, BI, and AI platform vendors. **Source:** https://gtm.ai/marketplace/data-scientists --- ## Overview Data Scientists are embedded across virtually every industry — in tech companies building ML products, in financial services running risk models, in healthcare analyzing clinical data, and in retail optimizing supply chains. With 202,000+ verified records, this audience represents the full breadth of the data science profession and is a core persona for analytics, BI, and AI tooling vendors. ## What's Included Verified contacts with Data Scientist or Senior Data Scientist titles. Direct email and phone with company firmographics for targeting by industry, company size, and geography. ## Use Cases - **Analytics and BI vendors** (Tableau, Looker, Mode) targeting data practitioners who evaluate and champion tools - **Python and notebook environments** (Databricks, JupyterHub) reaching data science teams - **AutoML and AI platforms** targeting data scientists looking to scale model development ================================================================================ # Defense Contractors in the DC Metro > 13,500+ defense, government services, and federal contracting companies in the Washington DC metro area **Source:** https://gtm.ai/marketplace/defense-contractors-dc --- ## Overview The Washington DC metro area — spanning Northern Virginia, Maryland, and DC proper — is the world's largest concentration of defense contractors, government services firms, and federal IT companies. This audience covers 13,500+ companies operating in the federal contracting ecosystem, enriched with agency focus (DoD, civilian agencies, intelligence community), known contract vehicles (SEWP, CIO-SP3, OASIS), NAICS codes, and revenue and contract backlog estimates. Contacts span both business development leaders who drive capture strategy and executive leadership who control vendor and partnership decisions. ## What's Included - **Company Identity**: Company name, website, and DC metro location (NoVa, Maryland, DC) - **Contract Focus**: Primary agency customer base (DoD, DHS, civilian agencies, IC/intelligence) and service category (IT, professional services, R&D, logistics) - **Workforce**: Employee count and known clearance levels (cleared workforce estimates) - **Contracting Context**: Known contract vehicles, NAICS codes, and small business status (8(a), SDVOSB, WOSB, etc.) - **Revenue**: Revenue range and contract backlog estimates - **Contacts**: BD VPs, Capture Managers, CEOs, and CTOs with verified email and phone ## Use Cases ### Federal IT and Cybersecurity Sales Zero trust architecture, cloud migration platforms, identity and access management, and cybersecurity solutions vendors sell into federal contractors both as end-customers and as partners who integrate or resell into the federal government. Clearance level and agency focus data identifies the highest-fit accounts for each type of engagement. ### GovTech Platform and SaaS Sales SaaS platforms seeking FedRAMP authorization or selling into SLED markets often partner with DC-area contractors who handle federal implementation and compliance. This audience identifies the systems integrators and technology services firms best positioned to serve as channel partners for GovTech SaaS products. ### Defense Contractor Partnership Development Prime contractors seeking teaming partners for large IDIQ vehicles target companies with complementary capabilities, contract vehicles, and clearance levels. Revenue and contract backlog estimates identify companies at the right scale for meaningful teaming relationships without direct competitive overlap. ### Government Consulting and Advisory Management consulting firms, strategy advisors, and specialized federal practice groups can use agency focus and service category to identify DC-area contractors facing business transformation, M&A activity, or competitive displacement in their core agency markets. ================================================================================ # Demand Generation and Growth Marketing Leaders > 37,800+ Demand Generation, Growth Marketing, and Head of Growth leaders — the performance marketing buyers responsible for pipeline generation and acquisition spend. **Source:** https://gtm.ai/marketplace/demand-gen-and-growth-marketing-leaders --- ## Overview Demand generation and growth marketing leaders own the performance marketing budget — paid acquisition, content distribution, ABM programs, and attribution infrastructure. With 37,800+ verified professionals in these roles, this audience captures the buyers who control the largest line items in the marketing tech budget and are constantly evaluating tools that can improve pipeline quality and conversion efficiency. ## What's Included Verified contacts with Demand Generation, Demand Gen, Growth Marketing, Head of Growth, or VP Growth titles. Direct email and phone, plus company firmographics for segmentation by industry, size, and geography. ## Use Cases - **ABM and intent-based advertising platforms** reaching their primary buyer persona - **Marketing analytics and attribution vendors** targeting demand gen leaders who own pipeline metrics - **Content and SEO platforms** selling to growth marketers building organic acquisition programs ================================================================================ # DevOps and Platform Engineers > 319,000+ DevOps Engineers, Platform Engineers, SREs, and Infrastructure Engineers — the practitioners building and maintaining the deployment, reliability, and infrastructure layers. **Source:** https://gtm.ai/marketplace/devops-and-platform-engineers --- ## Overview DevOps Engineers, Platform Engineers, and Site Reliability Engineers are the engineers responsible for the infrastructure that keeps software running — CI/CD pipelines, containerization, monitoring, and cloud cost management. With 319,000+ verified practitioners, this is one of the largest practitioner audiences in the software engineering space and a critical persona for infrastructure tooling vendors. ## What's Included Verified contacts with DevOps Engineer, Platform Engineer, Site Reliability Engineer, SRE, or Infrastructure Engineer titles. Direct email, phone, and company firmographics for targeting across company sizes and industries. ## Use Cases - **CI/CD and GitOps vendors** (GitHub Actions, GitLab, ArgoCD) reaching the practitioners managing deployment pipelines - **Observability and monitoring platforms** (Datadog, New Relic, Grafana) targeting the engineers who own reliability - **Cloud FinOps tools** selling cost optimization to the engineers responsible for cloud infrastructure spend ================================================================================ # DevOps Engineers at High-Growth Companies > 10,700+ DevOps Engineers at companies with 30%+ year-over-year headcount growth — verified contacts, infrastructure stack, and employer context **Source:** https://gtm.ai/marketplace/devops-engineers-high-growth --- ## Overview This audience covers 10,700+ DevOps Engineers at companies with 30% or more year-over-year headcount growth — a deliberate signal filter that targets the engineering practitioners most likely to be scaling infrastructure, evaluating new tooling, and making consequential platform decisions under time pressure. High-growth headcount is a strong proxy for infrastructure investment cycles: companies growing at this rate are consistently adding compute, expanding CI/CD pipeline capacity, and standardizing developer environments in ways that create active demand for DevOps tooling and platform services. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and tenure at current company - **Employer Data**: Company name, headcount growth rate, and employee count - **DevOps Stack**: Known tools across CI/CD, IaC, container orchestration, and cloud platforms - **Engineering Context**: Engineering team size and platform team structure where available ## Use Cases ### DevOps Tooling and CI/CD Platform Sales Target DevOps Engineers at high-growth companies scaling their continuous integration and deployment infrastructure. Growth rate filtering surfaces teams actively expanding pipeline capacity — the primary signal for CI/CD platform evaluation and vendor selection. ### Cloud Infrastructure and IaC Sales Reach DevOps Engineers managing cloud infrastructure at companies adding compute and engineering headcount at pace. Known cloud platform data identifies the provider ecosystem in play — AWS, Azure, or GCP — enabling ecosystem-aligned outreach. ### Platform Engineering Recruiting Source DevOps and platform engineers at high-growth companies for platform engineering, SRE, and infrastructure roles. Growth rate data identifies companies actively hiring in the engineering organization, increasing the likelihood of responsive candidates. ### Site Reliability and Observability Tools Identify DevOps Engineers at high-growth companies where reliability and observability requirements are intensifying as system complexity grows. Companies scaling rapidly are the primary buyers for APM, logging, and distributed tracing platforms. ================================================================================ # Digital Transformation Leaders > VP Digital Transformation, Chief Digital Officer, Head of Digital, and Director of Digital Innovation — the executives driving modernization programs at traditional and enterprise companies. **Source:** https://gtm.ai/marketplace/digital-transformation-leaders --- ## Overview Digital Transformation leaders hold explicit organizational mandates to modernize — they're replacing legacy systems, moving workloads to the cloud, digitizing business processes, and driving cultural change toward data-driven operations. They have large budgets, C-suite backing, and are among the most active enterprise technology buyers. With 35,000+ verified transformation leaders, this audience is a high-value target for any vendor selling modernization, cloud, or AI solutions. ## What's Included Verified contacts with Chief Digital Officer, VP Digital Transformation, Head of Digital, or Director of Digital Innovation titles. Direct email and phone with company firmographics. ## Use Cases - **Cloud and infrastructure modernization vendors** reaching executives with explicit transformation mandates - **Process automation and RPA platforms** selling to leaders digitizing manual enterprise workflows - **AI and analytics platforms** targeting the executives responsible for data-driven transformation programs ================================================================================ # Directors of IT at Healthcare Companies > 238,800+ Directors of IT and Information Technology at healthcare organizations — verified contacts, org type, and technology environment **Source:** https://gtm.ai/marketplace/directors-of-it-healthcare --- ## Overview This audience covers 238,800+ Directors of IT at healthcare organizations — the operational IT leadership layer responsible for system administration, vendor management, and technology implementation below the CIO or CISO. Organization type classification across hospitals, health systems, payers, and health tech companies is a critical segmentation dimension: IT needs and compliance requirements differ substantially across these sub-types, and messaging that works for a regional health system does not transfer to a health insurance payer or a digital health startup. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, IT functional scope, and tenure at current organization - **Organization Type**: Hospital, health system, payer, ambulatory, or health tech classification - **IT Environment**: Known EHR platform, infrastructure stack, and clinical technology in use - **Org Size**: Employee count and bed count for acute care providers ## Use Cases ### Healthcare IT Platform and EHR Sales Target IT Directors at hospitals and health systems who manage EHR implementation, integration, and vendor relationships. Organization type and known EHR data enable outreach segmented by platform ecosystem — Epic shops, Cerner/Oracle Health environments, and independent platforms each represent distinct sales motions. ### Cybersecurity and Compliance Solutions Reach IT leaders at healthcare organizations where HIPAA compliance, medical device security, and ransomware resilience are operational mandates. Organization size and type determine the specific compliance framework and budget scale in play. ### Cloud and Infrastructure Modernization Identify IT Directors at healthcare organizations transitioning legacy on-premise infrastructure to cloud or hybrid environments. Known tech stack data surfaces organizations still running aging infrastructure that represent modernization sales opportunities. ### Managed IT and Outsourcing Services Target IT Directors at smaller healthcare organizations — community hospitals, medical groups, independent clinics — where internal IT teams are lean and managed services provide a cost-effective alternative to full-time staff expansion. ================================================================================ # Directors of Procurement in Manufacturing > 29,900+ Directors of Procurement and Supply Chain at manufacturing companies — verified contacts, industry sub-sector, and supplier context **Source:** https://gtm.ai/marketplace/directors-of-procurement-manufacturing --- ## Overview This audience covers 29,900+ Directors of Procurement and Supply Chain at manufacturing companies, segmented by industry sub-sector — discrete manufacturing, process manufacturing, food and beverage, automotive, industrial equipment, and electronics. Sub-sector classification matters because procurement priorities, supplier relationships, and software ecosystems differ significantly across manufacturing categories. Revenue range and employee count data provide the organizational scale context needed to qualify accounts before outreach. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, procurement scope (direct, indirect, or both), and tenure - **Company Profile**: Company name, manufacturing sub-sector, employee count, and revenue range - **Procurement Scope**: Direct vs. indirect procurement responsibility and team size estimates - **Location Data**: HQ country and primary manufacturing site locations where available ## Use Cases ### Procurement Software and Sourcing Tools Target procurement directors at manufacturing companies evaluating spend management, e-sourcing, and supplier management platforms. Sub-sector filtering allows sellers to prioritize industries where their platform has existing customer references and vertical-specific features. ### Industrial Supplier and MRO Sales Reach procurement leaders who manage maintenance, repair, and operations supplier relationships at manufacturing facilities. Revenue and employee count data identify companies with the procurement volume to justify a structured MRO program. ### Supply Chain Consulting and Advisory Identify procurement directors at manufacturers navigating supplier risk, nearshoring, or cost reduction programs. Tenure data surfaces recently hired directors who are most likely to bring in outside advisory support during their first year. ### ERP and Operations Platform Sales Target procurement directors at manufacturing companies evaluating or replacing ERP and procurement modules. Manufacturing sub-sector classification enables vertical-specific ERP messaging — automotive suppliers have different requirements than food processors or electronics manufacturers. ================================================================================ # Directors of Product Management > 202,000+ Director-level product management professionals — the hands-on product leaders managing teams, roadmaps, and day-to-day tool decisions. **Source:** https://gtm.ai/marketplace/directors-of-product-management --- ## Overview Directors of Product Management are where product strategy meets execution — they're making tool decisions, running PM team rituals, and often the most influential evaluators for product management platforms. With 202,000+ verified Directors of Product globally, this audience represents both a large scale and a high-quality persona for product tooling vendors. ## What's Included Verified Director-level contacts in product management roles. Direct email, phone, and company firmographics for targeting by company size, industry, and geography. ## Use Cases - **PM workflow tools** (Jira, Linear, Notion, Confluence) targeting the Directors who configure and champion them - **Roadmapping and prioritization platforms** selling to Directors who own roadmap execution - **Customer analytics tools** reaching product leaders who drive data-informed decisions ================================================================================ # EdTech Companies Globally > 61,800+ education technology companies worldwide with funding history, employee count, and institutional vs consumer focus **Source:** https://gtm.ai/marketplace/edtech-companies-globally --- ## Overview Education technology spans four fundamentally different markets — K-12, higher education, corporate learning, and consumer — each with distinct buyers, procurement cycles, and willingness to pay. This audience covers 61,800+ EdTech companies globally across all four segments, enriched with sub-sector classification, geographic market focus, funding history, and verified executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and global headquarters - **Education Sub-Sector**: K-12, higher education, corporate training and L&D, professional certification, or consumer - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Geographic Focus**: Primary markets served (US, EMEA, APAC, LATAM, or global) - **Executive Contacts**: Verified email and phone for C-suite, VP of Product, VP of Sales, and VP of Partnerships ## Use Cases ### EdTech Platform and Content Sales Curriculum publishers, content licensing platforms, and assessment vendors should use sub-sector to target the right buyer — K-12 platforms have entirely different procurement structures than corporate L&D vendors. Geographic focus enables precise targeting for region-specific content or compliance requirements. ### Learning Management System Outreach LMS vendors and adjacent tools (authoring, analytics, integration middleware) can use this audience to identify EdTech companies that are themselves LMS buyers for internal training or white-label deployment. Sub-sector and headcount filter for the right company profile. ### Investor Deal Sourcing EdTech investors can filter by sub-sector, geography, and funding stage to surface companies in high-priority segments. Corporate learning and professional development are attracting significantly more institutional capital than consumer — use sub-sector to align to fund mandate. ### Corporate Training and L&D Partnerships Enterprise L&D platforms, skills assessment tools, and workforce development vendors can use the corporate training sub-sector filter to identify EdTech companies building for the same enterprise buyer set — surfacing both partnership candidates and competitive intelligence. ================================================================================ # Energy Companies in Houston > 28,100+ energy, oil and gas, and industrial companies in the Houston metro — the energy capital of the United States **Source:** https://gtm.ai/marketplace/energy-companies-houston --- ## Overview Houston is the energy capital of the United States, housing more energy company headquarters than any other city in the world. This audience covers 28,100+ energy, oil and gas, petrochemical, and industrial companies across the Houston metro, enriched with energy sub-sector classification (upstream E&P, midstream pipeline and storage, downstream refining, LNG, petrochemicals, oilfield services, and renewables), operational scope, and revenue data. The Houston energy market has undergone significant transformation over the past decade, with growing renewable energy and energy transition activity sitting alongside the traditional oil and gas core. ## What's Included - **Company Identity**: Company name, website, and HQ address - **Sub-Sector**: Energy classification (upstream/E&P, midstream, downstream/refining, oilfield services, LNG, petrochemicals, renewables/energy transition) - **Size**: Employee count and revenue range - **Operational Scope**: Geographic operating footprint (basin or geographic reach for field operations) - **Ownership**: Public, private, PE-backed, or JV structure - **Contacts**: CEO, CFO, CTO, VP Operations, and VP Engineering contacts with verified email and phone ## Use Cases ### Energy Technology and Software Sales Upstream software (production optimization, reservoir simulation), midstream operations platforms, ESG reporting tools, and energy data analytics vendors all have dense target markets in Houston. Sub-sector filters allow precise targeting — upstream E&P companies have fundamentally different software needs than midstream pipeline operators or downstream refiners. ### Industrial Equipment and Services Sales Industrial equipment, inspection services, corrosion control, and maintenance providers targeting the Houston energy sector can use employee count and operational scope to prioritize accounts by asset base and maintenance spend potential. Oilfield services companies are both buyers and competitor-adjacent vendors in this market. ### Houston Energy Market Entry Vendors new to the Houston energy market — whether domestic companies expanding their energy vertical or international vendors entering the US market — can use this audience to map the full addressable market, identify the highest-value accounts, and build an initial pipeline before establishing local sales presence. ### Investor Deal Sourcing in Energy Private equity firms, infrastructure funds, and energy-focused investors active in Houston can use ownership type, revenue, and growth data to identify acquisition targets, co-investment opportunities, and portfolio company adjacencies within the Houston energy ecosystem. ================================================================================ # Enrich Company > Look up a company's full profile by name, domain, ticker, or ZoomInfo ID — firmographics, financials, corporate structure, and growth signals in one place. **Source:** https://gtm.ai/marketplace/zoominfo-enrich-company --- ## Overview The Enrich Company skill pulls a company's full profile from ZoomInfo's database in response to a name, website domain, stock ticker, or ZoomInfo company ID. It returns everything a sales rep or account researcher needs to understand an account: firmographics, financials, corporate structure, employee growth trends, and how many contacts ZoomInfo has on file. This skill is designed for the moments when you need a complete picture of a specific company fast — qualifying an inbound lead, prepping for a discovery call, or filling in CRM fields before an account gets assigned. It handles the lookup automatically, falling back to a fuzzy company name search if an exact match isn't found, and presents results in a clean, scannable table. The returned ZoomInfo company ID can be used directly with other skills — passing it to Find Similar to discover lookalikes, or to Recommend Contacts to surface the right people at the account. ## What It Does - **Flexible lookup**: Accepts company name, domain, stock ticker, or ZoomInfo company ID — no need to know the exact format - **Firmographics**: Industry, sub-industries, employee count, revenue, business model (B2B/B2C/B2G), founding year, HQ location - **Financials & ownership**: Company type (public/private), stock ticker, SIC and NAICS codes, revenue band - **Corporate hierarchy**: Ultimate parent, immediate parent, and subsidiary count where available - **Growth signals**: 1-year and 2-year employee growth percentages, recent funding rounds - **Contact coverage**: Number of ZoomInfo contacts on record at the company ## Use Cases ### Inbound Lead Qualification When a new account comes in through a form, trial signup, or SDR-sourced lead, enrich it immediately to assess fit. Revenue, headcount, industry, and growth trajectory tell you whether to fast-track to AE or nurture differently. ### Pre-call Account Research Before a discovery call or executive briefing, pull the company's full profile to understand their structure, recent growth signals, and financial context. Walk in knowing the company's size, whether they're PE-backed, and how fast they're hiring. ### CRM Population Keep your CRM records complete and fresh. Paste a company name or domain and get all standard firmographic fields back — ready to copy into your CRM account record or feed into an enrichment workflow. ================================================================================ # Enrich Contact > Look up a person's full professional profile by email, name and company, LinkedIn URL, or phone number — title, department, contact details, and accuracy score. **Source:** https://gtm.ai/marketplace/zoominfo-enrich-contact --- ## Overview The Enrich Contact skill retrieves a person's full professional profile from ZoomInfo's database using whatever identifying information you have — an email address, a name and company, a LinkedIn URL, or a phone number. It returns verified contact details, role information, and company context in a structured profile, along with a data accuracy score so you know how fresh and reliable the record is. This skill is built for the practical realities of B2B sales and marketing research: you rarely have a ZoomInfo ID handy, and you often have only partial information. The skill resolves the best match from whatever input you provide, falls back to a search if enrichment doesn't find an exact match, and surfaces the most complete profile available. Accuracy score is surfaced prominently — anything below 80 is flagged — so you can prioritize outreach to the most reliable records and avoid bounced emails or disconnected numbers. ## What It Does - **Flexible lookup**: Accepts email, first/last name + company, full name + company, LinkedIn URL, phone number, or ZoomInfo person ID - **Verified contact details**: Direct work email, direct phone, mobile phone — each with ZoomInfo's contact accuracy scoring - **Role data**: Current job title, department, management level (C-Suite, VP, Director, Manager, etc.), and job function - **Company context**: Employer name, industry, and company size — so you can assess fit without a separate lookup - **Accuracy flagging**: Records below an 80% accuracy score are explicitly flagged to help you prioritize reliable data ## Use Cases ### Pre-outreach Research Before sending an email or making a call, look up the person to confirm their current title, get a verified direct dial, and check that the record is accurate enough to be worth reaching out to. Avoid bounces and wrong numbers before they happen. ### CRM Enrichment When contacts in your CRM are missing key fields — title, email, phone — enrich them one at a time or in batch. Paste a name and company to fill in the gaps and update records with ZoomInfo's verified data. ### Contact Verification Confirm that a contact you already have is still at the company and in the same role. The accuracy score and last-updated timestamp help you identify stale records that need re-verification or removal. ================================================================================ # Enterprise Architects and Technical Architects > Enterprise Architects, Solutions Architects, and Technical Architects — the infrastructure strategists who design system integrations, cloud architectures, and technology roadmaps. **Source:** https://gtm.ai/marketplace/enterprise-and-technical-architects --- ## Overview Enterprise and Technical Architects sit at the intersection of business requirements and technical infrastructure — they design the system architectures, integration patterns, and cloud strategies that technology investments are built on. When an Enterprise Architect recommends a platform, the organization usually follows. With 95,000+ verified architect contacts, this audience targets one of the most influential technical buying personas in enterprise technology. ## What's Included Verified contacts with Enterprise Architect, Solutions Architect, Technical Architect, or Principal Architect titles (non-sales). Direct email and phone with company firmographics. ## Use Cases - **Cloud and hybrid infrastructure vendors** reaching the architects who design and recommend platform choices - **Integration platforms** (MuleSoft, Boomi, Informatica) targeting architects who own integration strategy - **Security and zero-trust vendors** selling to architects responsible for enterprise security design ================================================================================ # Enterprise Companies in Singapore > 459+ enterprise companies with 1,000+ employees headquartered in Singapore — the gateway to Southeast Asia **Source:** https://gtm.ai/marketplace/enterprise-companies-singapore --- ## Overview Singapore serves as the regional headquarters for the majority of multinational companies operating in Southeast Asia, making it the highest-leverage entry point for vendors pursuing the APAC market. This audience covers 459+ enterprise companies with 1,000 or more employees headquartered in Singapore, enriched with industry classification, regional footprint data, revenue estimates, and executive team contacts. Companies in this audience span financial services, technology, consumer goods, logistics, healthcare, and professional services — reflecting Singapore's role as a hub for every major sector operating across ASEAN. ## What's Included - **Company Identity**: Company name, website, and Singapore HQ location - **Industry**: Sector classification and sub-sector - **Scale**: Employee count, regional office footprint (countries covered), and revenue estimates - **Regional Scope**: Whether the Singapore entity is a global HQ, regional HQ, or Asia-Pacific hub - **Contacts**: CEO, CFO, CTO, and VP-level executive contacts with verified email and phone ## Use Cases ### Southeast Asia Market Entry Companies entering Southeast Asia — whether expanding from North America, Europe, or other APAC markets — typically engage Singapore regional HQ contacts first. This audience provides the executive contact layer for the decision-makers who control APAC and ASEAN budgets, enabling structured market entry outreach before building country-level coverage. ### Singapore Enterprise Software Sales Enterprise software vendors with Singapore sales presence can use this audience to build comprehensive account coverage across all 459+ enterprise accounts, segment by industry and size, and prioritize territory assignments for local enterprise account executives. ### Regional Headquarters Outreach for APAC Expansion Vendors already operating in one ASEAN market and expanding regionally can use Singapore HQ contacts to engage the regional decision-makers who control multi-country deployments and APAC budget authority — often the most efficient path to regional enterprise contracts. ### Financial and Professional Services in Singapore Singapore is ASEAN's financial capital, housing the regional offices of global banks, asset managers, and professional services firms. Financial technology, compliance, legal, and advisory vendors can use this audience to target the financial services cluster within the broader enterprise population. ================================================================================ # Enterprise Sales Leaders (VP and Above) > Chief Revenue Officers, VPs of Sales, and SVPs of Sales at companies with 500+ employees — the senior revenue leaders managing large enterprise sales organizations. **Source:** https://gtm.ai/marketplace/enterprise-sales-leaders-vp-above --- ## Overview Chief Revenue Officers and VP-level sales leaders at enterprise companies are among the most targeted buyers in B2B — they control significant sales technology budgets, drive methodology decisions, and are the executive sponsors for major revenue platform investments. With 38,000+ verified senior sales leaders at companies with 500+ employees, this audience targets the top of the enterprise revenue leadership hierarchy. ## What's Included Verified CRO, SVP Sales, VP Sales, and Head of Sales contacts at companies with 500+ employees. Direct email and phone with company firmographics for targeting by industry, company size, and geography. ## Use Cases - **Revenue intelligence and pipeline management platforms** reaching the leaders who own forecast accuracy - **Sales compensation and quota management tools** targeting VPs responsible for territory and comp design - **Executive sales coaching and advisory** reaching senior leaders investing in team performance ================================================================================ # Enterprise Software Companies in the US > 3,100+ US enterprise software companies with $100M+ revenue, verified executive contacts, tech stack, and growth signals **Source:** https://gtm.ai/marketplace/enterprise-software-companies-us --- ## Overview This audience covers US enterprise software companies at scale — $100M+ in revenue, with established go-to-market motions, large customer bases, and complex technology ecosystems. These companies have real software budgets, multi-year procurement cycles, and named decision-makers with defined ownership over vendor relationships. The audience is built from ZoomInfo's identity graph and enriched with tech stack detection, org structure signals, and verified executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters location - **Financials**: Revenue estimates and employee count with year-over-year growth - **Executive Contacts**: Verified email and phone for C-suite and VP-level leaders across sales, marketing, IT, and operations - **Tech Stack**: Detected software tools across CRM, ERP, data infrastructure, and security functions - **Ownership Type**: Public, private equity-backed, founder-owned, or subsidiary classification - **Industry Sub-Classification**: Vertical software segment (ERP, HCM, CRM, analytics, security, etc.) ## Use Cases ### Enterprise SaaS Sales Build named account lists of the US enterprise software market segmented by vertical, revenue tier, or tech stack. Identify where incumbents are entrenched and where point solutions have gained foothold — both are selling opportunities. ### Channel Partnership Development Enterprise software companies are high-value channel partners — they have distribution, customer relationships, and integration ecosystems. Use this audience to identify companies whose customer base and product surface area align with a co-sell or OEM motion. ### M&A Target Sourcing Filter by revenue range, ownership type, and vertical to identify acquisition targets for software consolidators and PE-backed platform companies. Layer in growth rate and tech stack to prioritize fit. ### Competitive Landscape Analysis Map the enterprise software landscape by sub-category and revenue tier to identify how the market is structured and where consolidation is occurring. Track headcount growth as a leading indicator of competitive momentum. ================================================================================ # Ex-FAANG Executives at Startups > 68,000+ VP and C-suite leaders with verified past employment at Meta, Apple, Amazon, Netflix, or Google, now working at companies with fewer than 500 employees. **Source:** https://gtm.ai/marketplace/ex-faang-executives-at-startups --- ## Overview This audience captures senior leaders — VPs, Directors, and C-suite executives — who previously worked at one of the five largest consumer technology companies (Meta, Apple, Amazon, Netflix, Google) and have since moved to startups or growth-stage companies with fewer than 500 employees. These individuals carry enterprise-scale institutional knowledge into fast-moving environments, making them high-value targets for vendors, recruiters, and investors alike. ## What's Included Each record includes the contact's current role, employer, and verified contact details alongside resume history confirming their FAANG tenure. Management level and job function let you filter by discipline — engineering, product, sales, marketing, or operations. ## Use Cases - **Recruiting firms** sourcing enterprise-experienced operators for VC-backed companies - **B2B SaaS vendors** targeting buyers who've managed large budgets at scale - **Investors and advisors** mapping operator networks across the startup ecosystem ================================================================================ # Executives at Companies with Recent Funding > 20,200+ VP and C-suite executives at companies that closed a funding round in the last 90 days — fresh capital drives vendor evaluation **Source:** https://gtm.ai/marketplace/executives-companies-recent-funding --- ## Overview Funding events are one of the most reliable buying triggers in B2B — companies that close a round are actively building teams, expanding their tech stack, and scaling their go-to-market motion. This audience pairs funding event data with VP and C-suite contact records to give revenue teams a direct line to the decision-makers at freshly capitalized companies. Records are updated daily as funding announcements are processed and matched to executive contact data. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **Title and Seniority**: VP and C-suite title with functional classification (Sales, Marketing, Engineering, Finance, Operations, Product) - **Funding Details**: Round type (Seed, Series A, Series B, Series C, Growth, PE), round size, announcement date, and lead investors - **Company Profile**: Company name, industry, employee count at time of funding, and product/market category - **Stage and Velocity**: Funding stage and time between rounds where available — indicates growth velocity - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Post-Funding Executive Outreach The 0–60 day window after a funding close is peak vendor evaluation time. Companies are building headcount plans, issuing new tool contracts, and scaling infrastructure. Outreach that references the funding round specifically — and positions your product as the right investment to compound their new capital — consistently outperforms generic cold outreach. Use round size to calibrate deal size expectations and lead with ROI-per-headcount messaging for companies in aggressive hiring mode. ### Sales and Marketing Tool Sales at Funded Companies Newly funded companies are the most reliable segment for sales and marketing technology purchases. Series A and B companies are building their first scalable GTM motion and buying CRM, sales engagement, marketing automation, and intent data for the first time. Series C and growth-stage companies are scaling and optimizing — they're upgrading platforms and consolidating their stack. Target by stage to align your platform's ideal customer profile. ### Recruiting and Talent Services for Growing Teams Executive search firms, recruiting platforms, and employer branding services target newly funded companies as their highest-intent buyers. A Series B raise almost always precedes a significant hiring push — the Glassdoor listings appear within 30 days. Recruiting platform and ATS vendors, talent sourcing tools, and executive search firms should reach HR, Operations, and CEO contacts in the first 60 days post-announcement. ### Banking and Financial Services for Funded Startups Commercial banking, venture lending, and treasury management services target newly funded startups before the capital hits the bank. Relationship bankers, fintech lenders, and treasury services platforms use funding events as the primary trigger for outreach to CEO and CFO contacts. Filter by round size to align with credit and banking product thresholds — seed rounds for startup banking products, Series B+ for commercial credit and treasury services. ================================================================================ # Finance Leaders at PE Portfolio Companies > CFOs, VPs of Finance, and Controllers at private equity-backed companies — finance leaders operating under PE financial reporting and EBITDA improvement mandates. **Source:** https://gtm.ai/marketplace/finance-leaders-at-pe-portfolio --- ## Overview Finance leaders at PE-backed companies face a distinct set of pressures: PE sponsors require detailed financial reporting, EBITDA improvement plans, and often acquisition integration work. These CFOs and VPs of Finance are buying FP&A tools, reporting systems, and accounting infrastructure with urgency — they can't afford manual processes when boards are asking for board decks every month. With 12,000+ verified finance contacts at PE-backed companies, this audience combines the financial leadership persona with the PE investment signal. ## What's Included CFO, VP Finance, and Controller contacts at PE-backed companies. PE funding confirmation and company firmographics included alongside direct contact details. ## Use Cases - **FP&A and financial close platforms** targeting PE portfolio finance teams with reporting mandates - **Accounting and audit services** serving PE portfolio companies with compliance requirements - **ERP and operational finance tools** reaching the finance leaders responsible for PE-required modernization ================================================================================ # Financial Services Companies in London > 26,000+ financial services companies in London — the largest financial center in Europe, spanning banking, asset management, fintech, and insurance **Source:** https://gtm.ai/marketplace/financial-services-london --- ## Overview London is Europe's largest financial services center and a global hub for banking, asset management, insurance, and financial technology. This audience covers 26,000+ financial services companies across the City, Canary Wharf, and broader Greater London area — from global investment banks and Lloyd's syndicates to fintech startups and boutique asset managers. Each company is enriched with FCA and PRA regulatory status where applicable, financial sector classification, AUM or revenue estimates, and executive team contact information, enabling both broad market coverage and highly targeted regulatory or sector-specific campaigns. ## What's Included - **Company Identity**: Company name, website, and London area location - **Sector Classification**: Financial sector (retail banking, investment banking, asset management, insurance, fintech, payments, hedge funds, private equity) - **Size**: Employee count, year-over-year growth rate, and AUM or revenue estimates - **Regulatory Status**: FCA and/or PRA authorization status and regulated activities where available - **Contacts**: CEO, CFO, CTO, CCO, and other executive team members with verified email and phone ## Use Cases ### UK Fintech and Financial Platform Sales London's fintech ecosystem — one of the world's largest — is a primary market for payments infrastructure, open banking platforms, embedded finance tools, and financial data APIs. Sector and stage filters identify fintech companies at the growth phase where platform and infrastructure spend accelerates. ### London Financial Services Market Entry Non-UK vendors entering the London financial services market can use this audience to build a complete account list across every financial sector, size tier, and regulatory category — enabling territory planning, TAM analysis, and initial outreach campaign design for a structured market entry. ### Compliance and Regulatory Solutions for UK Firms Post-Brexit regulatory complexity has increased compliance burden for UK financial services firms significantly. RegTech, compliance monitoring, and regulatory reporting vendors can target CCOs and compliance leaders at FCA-regulated firms facing new UK-specific obligations diverging from EU frameworks. ### European Capital Markets and Advisory Investment banks, advisory firms, and capital markets platforms using London as their European hub can build institutional client lists across asset managers, hedge funds, and insurance companies — segmented by AUM and sector to prioritize by deal value and product fit. ================================================================================ # Financial Services Executives in New York City > 153,000+ VP and C-suite executives at financial services companies in the New York City metro — verified contacts and firm context **Source:** https://gtm.ai/marketplace/financial-services-executives-nyc --- ## Overview New York City is the world's largest financial services cluster, and this audience captures 153,000+ VP and C-suite executives across its full breadth — investment banks, private equity and credit firms, hedge funds, asset managers, insurance carriers, and fintech companies. Contacts are enriched with firm context including financial sector classification, AUM or revenue range where available, and career tenure, enabling precise segmentation across the deal cycle from initial outreach through executive relationship development. ## What's Included - **Identity**: Full name, verified email, direct phone, and LinkedIn profile URL - **Role Context**: Current title, management level (VP, MD, Partner, C-suite), and functional area - **Firm Context**: Firm name and financial sector classification (investment banking, private equity, hedge fund, asset management, insurance, fintech) - **Firm Size**: AUM or revenue range where disclosed, employee count - **Tenure**: Years at current firm and career history summary ## Use Cases ### Financial Technology and Data Sales Bloomberg Terminal alternatives, trading infrastructure, alternative data, and financial analytics platforms all compete for budget at NYC-based investment firms. Use management level and sector filters to build precise target lists — quant roles at hedge funds vs. operations leaders at insurance carriers have very different buying criteria. ### Wealth and Investment Management Services Prime brokerage, fund administration, tax advisory, and custody services target decision-makers at asset managers and private equity firms. AUM range and firm type attributes help prioritize accounts by deal size potential and service fit. ### Regulatory Compliance and Legal Services Law firms, RegTech vendors, and compliance consultancies can target CCOs, General Counsels, and risk executives at regulated financial institutions across NYC. Regulatory exposure varies significantly by firm type — this audience supports precise filtering. ### Executive Recruiting and Placement Search firms and talent networks placing C-suite and Managing Director-level executives into financial services can use tenure and career history data to identify passive candidates and warm-market targets across the NYC financial ecosystem. ================================================================================ # Find Similar > Find companies or contacts similar to a reference entity using ZoomInfo's ML-powered similarity model — ranked lookalikes for TAM analysis, territory expansion, and prospecting. **Source:** https://gtm.ai/marketplace/zoominfo-find-similar --- ## Overview The Find Similar skill uses ZoomInfo's machine learning similarity model to find companies or contacts that closely match a reference entity. Provide a company you've already won or a contact persona you're targeting, and the skill returns a ranked list of lookalikes — scored by how similar they are to the original, with pattern analysis to help you understand what the results have in common. For companies, this is the fastest way to clone a successful ICP across your total addressable market. For contacts, it powers multi-threading within an account by finding people at a target company who match the profile of contacts you've successfully engaged before. Both modes return up to 100 results by default, ranked by similarity score, with a summary of patterns across industry, geography, seniority, or company profile — so you can spot clusters and prioritize outreach accordingly. ## What It Does - **Company lookalike discovery**: Find companies similar to a reference by name, domain, or ZoomInfo company ID — ranked by ML similarity score - **Contact lookalike discovery**: Find contacts similar to a reference person by name + company, email, or ZoomInfo person ID — optionally scoped to a specific target account - **Similarity scoring**: Each result includes a score reflecting closeness to the reference entity, explained in terms of the reference attributes driving the match - **Pattern analysis**: Summarizes what the results have in common — dominant industries, geographies, seniority levels, or company sizes - **TAM and territory expansion**: Identify the full set of accounts that look like your best customers in new markets ## Use Cases ### ICP Cloning for Prospecting Take a company you've closed or are currently working and find 50 similar accounts. The skill reveals lookalikes you may never have considered — companies with matching industry profiles, headcount bands, tech stacks, and growth trajectories — so you can expand your pipeline without starting ICP research from scratch. ### Multi-threading Within a Target Account Given a champion or economic buyer you've successfully engaged at another company, find similar contacts at your target account. Scope results to a specific company to discover who in that organization matches the same persona — ideal for expansion plays or navigating an unfamiliar org chart. ### TAM and Territory Analysis Use a handful of your best-fit customers as seeds and run similarity searches to map the full landscape of accounts that share their profile. Use the pattern analysis to understand which verticals, geographies, or company types are most densely represented in your addressable market. ================================================================================ # Fintech Companies in New York City > 415+ fintech companies in the New York City metro — payments, lending, wealthtech, and financial infrastructure startups and scaleups **Source:** https://gtm.ai/marketplace/fintech-companies-new-york --- ## Overview New York City is the second-largest fintech hub in the world, anchored by a dense concentration of payments companies, lending platforms, wealthtech startups, and financial infrastructure providers. This audience covers 415+ fintech companies across the NYC metro — from early-stage startups in Brooklyn to growth-stage scaleups in Midtown. Records include funding data, employee growth signals, and verified executive contacts. ## What's Included - **Company Identity**: Name, website, LinkedIn, and primary office location within NYC metro - **Fintech Category**: Payments, lending, wealthtech, insurtech, infrastructure/API, or regtech classification - **Funding Profile**: Last round type, round size, total raised, and lead investors - **Employee Signals**: Current headcount and 6-month growth rate - **Regulatory Context**: Known licenses, charters, and regulatory registrations where available - **Executive Contacts**: C-suite and VP-level contacts with verified email and direct phone ## Use Cases ### NYC Fintech Ecosystem Sales New York's fintech cluster operates differently from Silicon Valley — it's closer to institutional finance, capital markets, and insurance. Target payments infrastructure vendors, B2B lending platforms, and wealthtech startups that need data, compliance tools, or financial APIs. Filter by funding stage to prioritize companies with capital to spend. ### Financial Infrastructure and API Sales Fintech companies in NYC are heavy buyers of developer infrastructure — payments APIs, identity verification, KYC/AML tooling, and banking-as-a-service platforms. Use this audience to reach CTO and VP Engineering contacts at companies actively building on financial infrastructure. ### Fintech Investor Deal Sourcing VC and PE firms covering NYC fintech can use this audience to map the ecosystem, identify early-stage companies before they appear in Crunchbase, and track headcount growth as a leading indicator of funding rounds. Filter by employee growth rate to surface the fastest-moving targets. ### Regulatory and Compliance Services NYC fintechs operate under complex regulatory frameworks — MSB licenses, broker-dealer registrations, state banking charters, and CFPB oversight. Legal, consulting, and compliance technology firms can target companies by regulatory status to identify those most likely to need compliance support. ================================================================================ # Former Frontier AI Lab Employees > 2,600+ professionals who previously worked at OpenAI, Anthropic, DeepMind, Mistral, or Cohere and have since moved to new roles across the industry. **Source:** https://gtm.ai/marketplace/former-frontier-ai-lab-employees --- ## Overview A rare and high-signal audience of professionals who built their careers at the leading frontier AI research organizations — OpenAI, Anthropic, DeepMind, Mistral, and Cohere — and are now applying that expertise at other companies. With just over 2,600 verified records globally, this is one of the most precise talent signals available for the AI industry. ## What's Included Records include verified resume history confirming employment at a frontier AI lab, current employer and role, and direct contact details. The audience spans researchers, engineers, product leaders, and go-to-market professionals who've made the transition out of labs. ## Use Cases - **AI vendors and tooling companies** recruiting engineers who understand model development - **Enterprise companies** building AI teams and seeking operators with research depth - **Investors** tracking where frontier AI talent is flowing post-lab ================================================================================ # Fortune 1000 Public Companies > All 1,000 Fortune-ranked US public companies with verified executive contacts, financials, org structure, and tech stack **Source:** https://gtm.ai/marketplace/fortune-1000-public-companies --- ## Overview The Fortune 1000 represents the highest-revenue US public companies across every industry — organizations with the largest technology budgets, the most complex buying committees, and the longest vendor relationships. This audience provides the full list with verified contacts at the executive and VP level, org structure data including subsidiaries and divisions, and tech stack signals across business-critical functions. Built from ZoomInfo's identity graph and refreshed daily against public filings, web signals, and contact verification. ## What's Included - **Company Identity**: Verified name, primary domain, NYSE/NASDAQ ticker, and Fortune rank - **Financials**: Annual revenue, employee count, and year-over-year growth from public filings - **Executive Contacts**: Verified email and phone for C-suite, EVP, and SVP-level leaders - **Org Structure**: Subsidiary and division mapping with revenue attribution where available - **Tech Stack**: Detected software tools across CRM, ERP, HCM, data, security, and marketing functions - **Budget Estimates**: Department-level budget estimates derived from headcount, revenue, and industry benchmarks ## Use Cases ### Enterprise Sales Targeting Build and prioritize named account lists across the Fortune 1000 using industry, revenue tier, and tech stack filters. Identify which accounts have the budget, the complexity, and the technology infrastructure to be ideal enterprise customers. ### Board and Executive Relationship Mapping Map the full executive and board composition of Fortune 1000 companies to support relationship-based enterprise sales, investor relations, and strategic partnership development. Verified contacts reduce research time and improve outreach precision. ### Technology Vendor Evaluation Understand what technology the Fortune 1000 is actually using — not what they report using. Tech stack signals derived from web crawl, job postings, and integration data provide a ground-truth view of the vendor landscape inside the largest US companies. ### Financial Benchmarking Use revenue, employee count, and department budget estimates to benchmark your existing customers against the broader Fortune 1000. Identify where you're over-indexed by industry and where you have penetration gaps. ================================================================================ # Founders at AI-Native Startups > Founders and CEOs at early-stage companies with AI or machine learning as a core part of their product — the builders creating the next generation of AI applications. **Source:** https://gtm.ai/marketplace/founders-at-ai-native-startups --- ## Overview AI-native startup founders are building businesses where AI is the product — not a feature layered on top. These founders are among the most active evaluators of AI infrastructure, compute, APIs, and developer tooling. They move fast, have strong opinions, and can become vocal advocates or critics for the tools they adopt. This audience captures the founder layer of the AI application ecosystem. ## What's Included Founder and CEO contacts at companies with AI or machine learning as a core component of their product classification. Direct contact details, company size, and funding status to help prioritize by stage and investment level. ## Use Cases - **AI API and platform vendors** (OpenAI, Anthropic, Cohere) reaching founders building on their APIs - **AI infrastructure vendors** (vector databases, model hosting, fine-tuning) targeting AI-first builders - **Investor networks** sourcing deal flow in the AI application and tooling layer ================================================================================ # Founders at Bootstrapped SMBs > 927,000+ founders and co-founders at bootstrapped companies with 10–250 employees and no recorded external funding — self-funded operators with full ownership and buying autonomy. **Source:** https://gtm.ai/marketplace/founders-at-bootstrapped-smbs --- ## Overview Bootstrapped founders at SMBs are among the most decisive buyers in B2B — they own 100% of the purchasing decision, they're self-sufficient by necessity, and they value ROI over brand. With 927,000+ founder contacts at companies with 10–250 employees and no recorded external funding, this audience is the core market for self-service SaaS, SMB financial services, and business tools designed for operators who don't have dedicated buyers. ## What's Included Verified founder and co-founder contacts at companies in the 10–250 employee range with no external funding recorded. Direct email, phone, and company firmographics for targeting by industry and company size. ## Use Cases - **SMB SaaS platforms** with self-serve or low-touch sales models targeting bootstrapped founder buyers - **SMB banking, lending, and financial services** reaching business owners who control company finances - **Business services** (accounting, legal, payroll) targeting founders handling these functions themselves ================================================================================ # Founders Who Have Exited (Post-Exit Operators) > Founders and CEOs with verified exit history — entrepreneurs who have sold or IPO'd a company and are now operators, investors, or building their next venture. **Source:** https://gtm.ai/marketplace/founders-who-have-exited --- ## Overview Post-exit founders represent one of the most valuable and hard-to-reach personas in the startup and investment ecosystem. They've successfully scaled and sold or IPO'd a company, and they're typically now deploying capital, advising, or building again — with the credibility and network that comes from having done it before. This audience of 18,000+ verified post-exit operators is identified through employment history showing founder roles at companies with documented acquisition or IPO events. ## What's Included Verified contacts with founder history at companies that have been acquired or have gone public, including current role and direct contact information. The audience spans operators who are now angel investors, venture partners, at their next company, or in advisory roles. ## Use Cases - **VCs and angel networks** sourcing deal flow from and building relationships with successful repeat operators - **Startup community platforms** recruiting post-exit founders as mentors, advisors, or EIRs - **High-value B2B vendors** reaching operators who have authority, capital, and low tolerance for bad software ================================================================================ # General Counsel and VP Legal > 134,000+ General Counsel, Chief Legal Officers, VP Legal, and Head of Legal contacts — the executive legal buyers responsible for contracts, compliance, and legal technology investments. **Source:** https://gtm.ai/marketplace/general-counsel-and-vp-legal --- ## Overview General Counsel and VP Legal leaders are the primary buyers for legal technology — contract lifecycle management, e-signature, legal operations platforms, and compliance software. They're also key stakeholders in any vendor relationship that involves data processing agreements or privacy compliance. With 134,000+ verified legal leaders globally, this audience spans the full range from startup first-hire GCs to Fortune 500 CLOs managing large legal departments. ## What's Included Verified contacts with General Counsel, Chief Legal Officer, VP Legal, or Head of Legal titles. Direct email and phone with company firmographics for targeting across company sizes and industries. ## Use Cases - **Contract lifecycle management and legal ops platforms** reaching the in-house buyers making tool decisions - **Compliance and regulatory technology vendors** targeting GCs responsible for legal risk management - **Law firms and outside counsel** prospecting in-house legal leaders for litigation, M&A, and transactional work ================================================================================ # Global Expansion Leaders > VP International, Head of Global Expansion, and Managing Directors responsible for international market entry — the leaders planning and executing cross-border growth strategies. **Source:** https://gtm.ai/marketplace/global-expansion-leaders --- ## Overview Global expansion leaders are managing the complexity of international operations — local hiring compliance, multi-currency payroll, cross-border payments, tax and regulatory requirements, and market localization. With 15,000+ verified professionals in international expansion and regional MD roles, this audience is the primary buyer for the global operations infrastructure market. ## What's Included Verified contacts with VP International, Head of Global Expansion, Managing Director (international market), or VP EMEA/APAC/LATAM titles. Direct email and phone with company firmographics. ## Use Cases - **Employer of record and global HR platforms** (Deel, Remote, Rippling Global) reaching the leaders managing international headcount - **Cross-border payments and FX platforms** targeting leaders managing multi-currency financial operations - **Legal and regulatory compliance vendors** for companies navigating international market entry ================================================================================ # Growth-Stage Healthcare Technology Companies > Venture-backed healthcare technology companies in the 50–500 employee range — digital health and healthtech innovators building the future of healthcare delivery, data, and operations. **Source:** https://gtm.ai/marketplace/growth-stage-healthtech-companies --- ## Overview Growth-stage healthcare technology companies are building at the intersection of software and one of the most complex, regulated industries in the world. They're scaling their teams and technology stacks while navigating HIPAA compliance, EHR integrations, and FDA regulatory requirements. With 3,500+ qualifying companies, this audience targets the digital health innovators who are actively investing in compliant, scalable infrastructure. ## What's Included Company records combining healthtech industry classification, venture funding confirmation, and 50–500 employee range. Funding stage, amount, and company description included. ## Use Cases - **Healthcare data and interoperability vendors** (FHIR APIs, EHR integrations) reaching companies building on health data - **Compliance and regulatory platforms** targeting healthtech companies managing HIPAA and clinical data compliance - **Healthcare-specific cloud security vendors** selling to companies handling protected health information at scale ================================================================================ # Growth-Stage SaaS Companies (50 to 200 Employees) > 18,000+ SaaS companies in the 50–200 employee range — past early stage, actively scaling GTM, and in peak tool-buying mode **Source:** https://gtm.ai/marketplace/saas-growth-stage-companies --- ## Overview The 50–200 employee range is the peak buying window for SaaS companies — they've validated product-market fit, raised a Series A or B, and are actively building their first scalable GTM motion. These companies are simultaneously buying CRM, marketing automation, intent data, RevOps infrastructure, HR platforms, and financial systems for the first time. They have the budget (venture-backed), the urgency (investor timeline pressure), and the technical sophistication to evaluate and close quickly. ## What's Included - **Company Identity**: Name, website, LinkedIn, and primary HQ location - **Growth Signals**: Current employee count, 6-month and 12-month headcount growth rate, and job posting velocity - **Funding Profile**: Stage (Series A, B, C), total raised, last round date, and lead investors - **Revenue Tech Stack**: Known CRM, marketing automation, sales engagement, and data tools from technographic signals - **Revenue Estimates**: Modeled ARR range based on headcount, stage, and category benchmarks - **Decision-Maker Contacts**: CEO, CRO, CMO, VP of Sales, VP of Marketing, and Head of RevOps with verified email and phone ## Use Cases ### Growth-Stage SaaS Sales 50–200 employee SaaS companies are in active evaluation mode across virtually every software category. They've outgrown their seed-stage tools (spreadsheets, free tiers, Zoho) and are standardizing on their first enterprise stack. Use employee count growth rate to prioritize companies that are scaling fastest — these are the ones with the most acute tool gaps and the strongest internal urgency to close. ### Integration and Partnership Development SaaS companies in this range are actively building their integration ecosystem — connecting their platform to Salesforce, HubSpot, Slack, and other infrastructure tools their customers use. Integration platform vendors, iPaaS providers, and SaaS companies seeking distribution partnerships can use this audience to identify companies at the stage where integration strategy becomes a product priority. ### Investor Deal Sourcing VC and growth equity firms can use this audience to map the competitive landscape within their target verticals, identify companies approaching Series B or Series C thresholds based on headcount growth signals, and benchmark portfolio companies against the broader market. Employee count growth rate and funding recency are the two most useful signals for identifying pre-announcement funding candidates. ### Competitive Intelligence SaaS companies can use this audience to track competitors, map adjacent markets, and identify acquisition targets. Technographic data reveals which tools companies are using — a useful signal for understanding competitive positioning and partnership opportunities. Filter by product category and funding stage to build a targeted competitive landscape within a specific market segment. ================================================================================ # Head of Engineering at Startups > VP Engineering and Head of Engineering leaders at companies with fewer than 200 employees — the first engineering leaders who set the foundational stack and culture for growing teams. **Source:** https://gtm.ai/marketplace/head-of-engineering-at-startups --- ## Overview The first VP of Engineering or Head of Engineering at a startup is one of the most consequential technical hires — they select the cloud provider, set the engineering culture, and make the tool decisions that will define the team for years. With 45,000+ verified engineering leaders at companies under 200 employees, this audience captures the founding engineering decision-makers who are shaping the next generation of tech stacks. ## What's Included VP Engineering and Head of Engineering contacts at companies with fewer than 200 employees. Direct email, phone, and company firmographics. The audience spans technology, fintech, healthcare tech, and any industry building software products. ## Use Cases - **Cloud providers and infrastructure vendors** reaching the first engineering leaders setting foundational architecture - **Developer tools** (monitoring, CI/CD, code collaboration) targeting startup VPs who become long-term champions - **Recruiting and talent platforms** reaching engineering leaders who are constantly hiring in growth mode ================================================================================ # Head of Finance at Startups (Series A–C) > VP Finance, Head of Finance, and Controller at venture-backed companies between Series A and Series C — the first financial leaders formalizing accounting, reporting, and FP&A at scaling startups. **Source:** https://gtm.ai/marketplace/head-of-finance-at-startups --- ## Overview The VP Finance or Head of Finance at a Series A–C company is often the first professional financial leader — they're building the accounting infrastructure, implementing FP&A, managing investor reporting, and making the tool decisions that will scale the finance function through the next funding rounds or IPO. These 14,000+ finance leaders are at a critical decision-making moment for financial tooling. ## What's Included VP Finance, Head of Finance, and Controller contacts at venture-backed companies between Series A and Series C. Company funding stage and firmographics included alongside direct contact details. ## Use Cases - **Financial close and accounting automation** vendors reaching first-time finance leaders replacing manual processes - **FP&A platforms** targeting startup finance teams building their planning infrastructure - **Startup banking and treasury platforms** reaching finance leaders managing company liquidity and spend ================================================================================ # Head of Growth at B2B SaaS Companies > VP Growth, Head of Growth, and Growth Lead contacts at B2B SaaS companies — the practitioners driving product-led and marketing-led growth experiments at scaling software businesses. **Source:** https://gtm.ai/marketplace/head-of-growth-b2b-saas --- ## Overview Growth leaders at B2B SaaS companies own the experimentation engine — they're running activation tests, optimizing onboarding funnels, managing PLG conversion rates, and driving expansion revenue through in-product signals. With 15,000+ verified VP and Head of Growth contacts at software companies, this audience targets the practitioners who are building and iterating on the growth machinery that drives compounding SaaS revenue. ## What's Included Verified contacts with VP Growth, Head of Growth, Director of Growth, or Growth Lead titles at B2B SaaS companies. Direct email and phone with company firmographics. ## Use Cases - **PLG analytics platforms** (Amplitude, Mixpanel, Pendo) targeting hands-on growth practitioners - **A/B testing and experimentation platforms** reaching the growth leaders running constant optimization cycles - **Onboarding and activation tools** selling to growth leaders responsible for improving time-to-value ================================================================================ # Heads of Analytics and Business Intelligence > VP Analytics, Head of Analytics, Head of Business Intelligence, and Director of BI leaders — the buyers responsible for data analytics infrastructure and the tools their teams use. **Source:** https://gtm.ai/marketplace/heads-of-analytics-bi --- ## Overview Heads of Analytics and Business Intelligence are the primary buyers for BI platforms, data visualization tools, and self-service analytics infrastructure. They sit between the data engineering team that builds pipelines and the business users who consume insights — and they're responsible for both the tooling and the culture of data-driven decision-making. With 42,000+ verified analytics leaders, this audience covers the buyers who control analytics platform decisions. ## What's Included Verified contacts with VP Analytics, Head of Analytics, Head of Business Intelligence, or Director of BI titles. Direct email and phone with company firmographics. ## Use Cases - **BI and data visualization platforms** (Tableau, Looker, Power BI) reaching the leaders who evaluate and champion analytics tools - **Semantic layer and data modeling vendors** targeting analytics leaders responsible for metrics definitions - **Embedded analytics vendors** reaching heads of analytics building reporting for internal stakeholders ================================================================================ # Heads of Content and VP Content > 10,800+ Chief Content Officers, VP Content, and Head of Content leaders — the buyers responsible for content strategy, production, SEO, and the content marketing technology stack. **Source:** https://gtm.ai/marketplace/heads-of-content --- ## Overview Content leaders are among the most active buyers of marketing technology — they're managing CMS platforms, SEO tools, content analytics, distribution channels, and increasingly AI writing assistants. With 10,800+ verified Head of Content and VP Content professionals globally, this audience targets the buyers who own content strategy and the tools that power it. ## What's Included Verified contacts with Chief Content Officer, VP Content, VP of Content, Head of Content, or Director of Content titles. Direct email and phone with company firmographics. ## Use Cases - **CMS and content platform vendors** reaching the leaders who select and manage content infrastructure - **SEO and content analytics tools** selling to content executives responsible for organic traffic and authority - **AI writing and content production platforms** targeting content leaders facing scale and efficiency pressure ================================================================================ # Heads of Design and VP Design > 20,100+ Chief Design Officers, VP Design, and Head of Design leaders — the executive design buyers responsible for product UX, design systems, and the visual direction of digital products. **Source:** https://gtm.ai/marketplace/heads-of-design --- ## Overview Design leaders — VPs of Design, Heads of Design, and Chief Design Officers — are the executive buyers for design tooling, UX research platforms, and design system infrastructure. They bridge product strategy and user experience, and they're increasingly influential in broader product and engineering technology decisions. With 20,100+ verified design leaders globally, this audience is essential for any vendor selling into design-driven organizations. ## What's Included Verified contacts with Chief Design Officer, VP Design, VP of Design, Head of Design, or Director of Design titles. Direct email and phone with company firmographics for targeting by company size and industry. ## Use Cases - **Design tools and prototyping platforms** reaching the leaders who champion and budget for design tooling - **UX research and usability testing vendors** selling to design executives responsible for user insight programs - **Design system and token management platforms** targeting design leaders building scalable component libraries ================================================================================ # Heads of Partnerships and Business Development > 40,300+ VP Partnerships, Head of Partnerships, VP Business Development, and Director of Partnerships contacts — the leaders building ecosystem strategies and strategic alliances. **Source:** https://gtm.ai/marketplace/heads-of-partnerships-biz-dev --- ## Overview Partnerships and Business Development leaders are the architects of company ecosystems — they negotiate technology integrations, build reseller and referral programs, and manage strategic alliances that drive indirect revenue. With 40,300+ verified partnerships and BD leaders, this audience is the primary buyer for partner relationship management (PRM) platforms, ecosystem data tools, and partnership consulting. ## What's Included Verified contacts with VP Partnerships, Head of Partnerships, VP Business Development, Head of Business Development, or Director of Partnerships titles. Direct email and phone with company firmographics. ## Use Cases - **PRM and partner ecosystem platforms** (PartnerStack, Allbound, Impact) targeting their primary buyer - **Technology integration and marketplace vendors** reaching BD leaders responsible for building integrations - **Partnership consulting and advisory firms** prospecting leaders who need strategic ecosystem guidance ================================================================================ # Heads of People at Series A and B Companies > People Operations leaders at companies that have raised a Series A or B — HR leaders tasked with building people infrastructure from scratch at fast-growing startups. **Source:** https://gtm.ai/marketplace/heads-of-people-series-a-b --- ## Overview The Head of People at a Series A or B company has one of the most demanding mandates in startups: build the HR function from near-zero while the company scales 50–200% in headcount. They're making foundational decisions about HRIS, payroll, benefits, and culture tools that will define the people infrastructure for years. This audience combines funding stage with people leadership to capture that exact buying moment. ## What's Included People Operations and HR leadership contacts at companies with confirmed Series A or B funding within the past two years. Direct contact details and company funding data included. ## Use Cases - **HRIS and HR platform vendors** targeting first-time formal HR buyers at funded startups - **Benefits brokers and platforms** reaching people leaders building benefits programs for the first time - **Employer of record and global HR vendors** targeting companies scaling internationally post-funding ================================================================================ # Heads of Product at Tech Companies > 45,000+ VP of Product, Head of Product, and CPO-level contacts at technology companies — the roadmap owners and platform decision-makers **Source:** https://gtm.ai/marketplace/heads-of-product-tech-companies --- ## Overview Product leaders at technology companies are the primary buyers of product analytics, experimentation platforms, roadmap tools, UX research software, and developer productivity tooling. This audience covers 45,000+ VP of Product, Head of Product, Director of Product, and CPO-level contacts globally — filtered to technology companies where product is a core organizational function with dedicated tooling budgets. Records include product category context and known analytics stack signals to enable precise targeting. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **Title and Scope**: Full title (CPO, VP of Product, Head of Product, Director of Product) and product org scope where available - **Company Context**: Company name, product category (B2B SaaS, consumer, developer tools, infrastructure, platform), and employee count - **Engineering Team Context**: Engineering team size signals as a proxy for product org scale and tooling complexity - **Product and Analytics Stack**: Known tools (Amplitude, Mixpanel, Jira, ProductBoard, Figma, Segment, LaunchDarkly) from technographic signals - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Product Analytics and Experimentation Tool Sales Product analytics platforms, A/B testing tools, and feature flagging systems target VP and Head of Product contacts as the primary buyer — often with engineering leadership as a secondary stakeholder. Use the known analytics stack to identify displacement opportunities (companies using legacy tools like Adobe Analytics or custom-built solutions) and personalize outreach around the specific analytics maturity challenges your platform addresses. ### Roadmap and Project Management Platform Sales Roadmap tools, product management platforms, and engineering-product collaboration software target Head of Product contacts as the primary evaluator. These tools are purchased at the product org level, with CEO or COO sign-off at smaller companies and VP approval at larger ones. Filter by company stage — early-stage companies are buying for the first time, while growth-stage companies are replacing tools they've outgrown. ### Design and Research Tool Sales UX research platforms, design systems tools, and customer feedback software target product leadership as the economic buyer. The Head of Product controls the budget for design and research tooling at most tech companies — even when the day-to-day evaluators are designers and researchers. Use company size and product category to prioritize the highest-fit targets for your design and research platform. ### Product Leadership Community Outreach Product management communities, CPO roundtables, and product leadership development programs target this audience for membership recruitment, event invitations, and thought leadership distribution. Product leaders are among the most active professional communities online — they seek peer benchmarks on roadmap prioritization, team structure, and tooling decisions. Filter by company stage and industry to build cohort-appropriate communities. ================================================================================ # Healthcare Companies in Texas > 407,000+ healthcare companies in Texas — one of the largest and most diverse state-level healthcare markets in the US **Source:** https://gtm.ai/marketplace/healthcare-companies-texas --- ## Overview Texas is one of the largest healthcare markets in the United States, with a population exceeding 30 million and major medical centers in Houston (Texas Medical Center — the world's largest), Dallas-Fort Worth, San Antonio, and Austin. This audience covers 407,000+ healthcare companies across the state, spanning providers (hospitals, physician groups, specialty clinics), payers (health plans, managed care organizations), pharmaceutical and biotech companies, and health technology vendors. Company growth rate and revenue data enable prioritization by account potential, while HQ city supports territory segmentation across Texas's geographically dispersed healthcare market. ## What's Included - **Company Identity**: Company name, website, and HQ city - **Sub-Sector**: Healthcare classification (hospital/health system, physician group, payer/MCO, pharma, medical device, health tech, home health, behavioral health) - **Size**: Employee count and revenue range - **Geography**: HQ city and regional presence across Texas metros (Houston, DFW, Austin, San Antonio, West Texas) - **Contacts**: Key executive contacts (CEO, CIO, CMO, CFO, COO) with verified email and phone - **Growth**: Year-over-year employee and revenue growth rates ## Use Cases ### Texas Regional Health Tech Sales Health IT vendors, EHR companies, telehealth platforms, and revenue cycle management tools targeting Texas can segment by sub-sector, company size, and metro area to build territory-aligned account lists and prioritize by healthcare IT spend potential. Texas's scale and fragmentation create a particularly rich target base for mid-market health tech. ### Healthcare Consulting and Advisory Strategy consulting, operations improvement, and value-based care advisory firms targeting Texas healthcare organizations can use revenue range and sub-sector filters to identify accounts with the scale and complexity that justifies external consulting engagement — typically health systems with $500M+ revenue or large physician group networks. ### Medical Device and Supply Sales Medical device manufacturers, surgical supply distributors, and capital equipment vendors can use sub-sector and employee count to identify hospital and ASC accounts by size and specialty focus. Texas Medical Center alone represents dozens of major hospital accounts with significant capital equipment budgets. ### Healthcare Workforce and Staffing Texas faces significant healthcare workforce shortages, particularly in rural markets and for specialized clinical roles. Staffing agencies, locum tenens networks, and healthcare recruiting firms can use this audience to target HR and operations leaders at healthcare organizations with active or anticipated workforce gaps. ================================================================================ # Healthcare Executives in the Southeast US > 96,600+ VP and C-suite healthcare executives across Florida, Georgia, North Carolina, Tennessee, and neighboring Southeast states **Source:** https://gtm.ai/marketplace/healthcare-executives-southeast --- ## Overview This audience covers VP and C-suite executives at healthcare organizations across the Southeast United States, including Florida, Georgia, North Carolina, Tennessee, Alabama, South Carolina, and Mississippi. The Southeast is one of the fastest-growing regions for healthcare delivery due to demographic shifts and population migration, making it a high-priority target for health tech vendors, device manufacturers, and service providers. Organization type spans hospitals, integrated health systems, managed care and payer organizations, and health technology companies. ## What's Included - **Identity**: Full name, verified email, direct phone, and LinkedIn profile URL - **Role Context**: Current title, management level (VP, SVP, C-suite), and functional area (operations, clinical, technology, finance) - **Organization Type**: Hospital, health system, payer/insurer, physician group, or health tech company - **Geography**: State and metro area within the Southeast - **Org Size**: Employee count and annual revenue estimates ## Use Cases ### Regional Health Tech and EHR Sales Southeast health systems are active buyers of clinical and administrative software, particularly as consolidation creates larger integrated delivery networks with centralized IT budgets. Target CIOs, CMIOs, and VP-level operations leaders driving EHR migrations, telehealth expansion, and patient engagement platform decisions. ### Healthcare Consulting and Advisory Strategy consulting, revenue cycle management, and operational improvement firms can identify VP and C-suite decision-makers at Southeast hospitals and health systems undergoing margin pressure, post-merger integration, or value-based care transformation. ### Medical Device and Equipment Sales Capital equipment and medical device sales teams targeting Southeast hospital networks can use management level and org size filters to prioritize high-value accounts and route contacts to the appropriate field rep by state or metro. ### Healthcare Workforce and Staffing Services The Southeast faces acute nursing and physician shortages in many markets. Workforce solutions, contract staffing, and talent acquisition firms can target CHROs, CMOs, and operations leaders at hospitals and health systems most likely to have active staffing needs. ================================================================================ # Healthcare IT Companies > 5,800+ companies at the intersection of healthcare and technology — EHR vendors, health tech platforms, and digital health startups **Source:** https://gtm.ai/marketplace/healthcare-it-companies --- ## Overview Healthcare IT sits at the intersection of clinical workflow, regulatory compliance, and enterprise software — a market with high switching costs, long procurement cycles, and increasing investment from both institutional buyers and venture capital. This audience covers 5,800+ companies globally across EHR, revenue cycle management, population health, digital health, telehealth, and healthcare data platforms, with verified funding data and executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and headquarters location - **Sub-Sector**: EHR, RCM, population health, telehealth, digital health, health data, or health IT services - **Platform Focus**: Primary software product or service category and target customer (payer, provider, patient) - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Executive Contacts**: Verified email and phone for C-suite, Chief Medical Officer, VP of Product, and VP of Sales ## Use Cases ### Health Tech Vendor Sales Selling into health tech companies requires understanding the buyer's customer — a company selling to hospitals has different infrastructure needs than one selling to consumers. Sub-sector and platform focus data enables precise vendor-to-vendor outreach with relevant positioning. ### Clinical and Administrative Software Sales Data, analytics, interoperability, and workflow automation vendors targeting health IT companies should filter by platform focus and customer type. A revenue cycle management platform needs different data infrastructure than a telehealth provider. ### Partnership and Integration Development HL7, FHIR, and API-based integrations are the connective tissue of health IT. Companies building integration infrastructure or data exchange platforms can use sub-sector classification to identify the highest-value partnership targets in the ecosystem. ### Investor Deal Sourcing in Digital Health Digital health investors can filter by sub-sector, funding stage, and headcount growth to identify companies gaining traction in high-priority categories. Recent funding data helps map which areas of the market are attracting capital and which are underinvested. ================================================================================ # High-Growth Startups (50%+ Headcount Growth) > 75,000+ companies with 50%+ year-over-year headcount growth — the fastest-scaling organizations actively hiring and expanding their tool stack **Source:** https://gtm.ai/marketplace/high-growth-startups-50pct --- ## Overview Headcount growth is one of the strongest leading indicators of software spend. Companies growing headcount at 50%+ year-over-year are actively building out departments, evaluating new tools, and expanding their operational infrastructure. This audience captures 75,000+ globally distributed high-growth companies filtered to verified 50%+ YoY headcount growth, enriched with funding data, hiring velocity by function, and decision-maker contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and headquarters location - **Growth Rate**: Verified 1-year headcount growth percentage derived from LinkedIn signals and job posting data - **Current Headcount**: Employee count with department-level breakdown - **Funding Profile**: Stage, total raised, and most recent round date - **Hiring Velocity**: Open roles and hiring rate by function (engineering, sales, marketing, ops) - **Decision-Maker Contacts**: Verified email and phone for C-suite, VP, and director-level contacts ## Use Cases ### High-Velocity Sales Targeting Companies scaling headcount at 50%+ are evaluating new tools on compressed timelines. Use growth rate and hiring velocity by department to identify which function is driving expansion — a company adding 30 sales reps in 90 days needs CRM, enablement, and compensation tooling now. ### Recruiting and Staffing Services High-growth companies are the highest-volume recruiters in the market. Filter by department hiring velocity and growth stage to identify clients who need staffing, recruiting technology, or employer branding services. ### SaaS Tool Expansion Sales Existing customers growing at this rate represent natural expansion opportunities. Use the audience to identify which of your current accounts have hit the high-growth threshold — and which new prospects are scaling into your product's enterprise tier. ### VC Deal Sourcing Headcount growth is a proxy for traction. Filter this audience by stage (seed, Series A) to surface early-stage companies showing exceptional velocity before they raise their next round and get picked up by competitive deal flow. ================================================================================ # HR Tech Companies in the US > 11,700+ US human resources technology companies spanning recruiting, HRIS, payroll, benefits, and workforce management **Source:** https://gtm.ai/marketplace/hr-tech-companies-us --- ## Overview The US HR tech market is one of the most active categories in B2B software — spanning applicant tracking systems, HRIS platforms, payroll processors, benefits administration, workforce management, and employee engagement tools. This audience covers 11,700+ US HR tech companies with category classification, customer segment targeting (SMB vs. enterprise), funding data, and verified C-suite contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters - **HR Tech Category**: ATS, HRIS, payroll, benefits administration, workforce management, engagement, or learning - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Customer Segment**: Primary customer size focus — SMB (1–100 employees), mid-market (100–1,000), or enterprise (1,000+) - **Executive Contacts**: Verified email and phone for CEO, CHRO, CTO, VP of Sales, and VP of Partnerships ## Use Cases ### HR Tech Integration and Partnership Sales Payroll providers, benefits carriers, and background check vendors seeking to integrate with HRIS and ATS platforms can use category and customer segment to identify the right integration partners. HR tech companies serving the same customer segment are the highest-value integration targets. ### Employer Benefits and Services Sales Insurance carriers, benefits brokers, and financial wellness platforms can use customer segment and headcount to identify HR tech companies with employer relationships in their target size range. These companies are often the distribution channel — not just the buyer. ### Payroll and Compliance Solutions Tax compliance, garnishment processing, and payroll audit vendors can use the payroll category filter to identify HR tech companies that need compliance infrastructure. Funding stage helps prioritize companies that are actively investing in platform robustness over those in maintenance mode. ### Investor Deal Sourcing HR tech investors can filter by category, customer segment, and funding stage to identify companies gaining traction in specific market segments. Customer segment is a critical variable — SMB HR tech has very different economics and exit multiples than enterprise HRIS platforms. ================================================================================ # HubSpot + Salesforce Stack > 3,400+ companies running both HubSpot and Salesforce — organizations navigating dual CRM environments, often signals of recent migration, multi-team deployment, or integration complexity. **Source:** https://gtm.ai/marketplace/hubspot-salesforce-stack --- ## Overview Companies running both HubSpot and Salesforce are almost always in a state of transition or deliberate separation — marketing uses HubSpot while sales runs Salesforce, or the company is mid-migration, or they've built a custom integration to sync the two. This 3,400+ company audience represents a clear pain point for integration, data quality, and CRM consolidation vendors. ## What's Included Confirmed dual installations of HubSpot and Salesforce with company firmographics. The audience spans growth-stage through mid-market companies, often with distinct marketing and sales technology stacks that need to be reconciled. ## Use Cases - **CRM integration and sync vendors** (PieSync, operations tools) targeting dual-CRM pain points - **RevOps consultants** offering stack consolidation and optimization - **Data quality vendors** addressing the sync and deduplication challenges between two CRM systems ================================================================================ # HubSpot + Zendesk Stack > 11,900+ companies running both HubSpot and Zendesk — the definitive mid-market customer lifecycle stack combining marketing/CRM with customer support. **Source:** https://gtm.ai/marketplace/hubspot-zendesk-stack --- ## Overview HubSpot + Zendesk is the canonical mid-market customer lifecycle stack: HubSpot runs pre-sale CRM and marketing automation while Zendesk handles post-sale support. Companies running both have distinct pre-sale and post-sale data siloes — and are active buyers of tools that bridge or unify the two. With 11,900+ confirmed dual installations, this is a large and well-defined target audience. ## What's Included Confirmed installations of both HubSpot and Zendesk with company firmographics. The audience is concentrated in SMB and mid-market technology, e-commerce, and SaaS companies with both marketing and customer success functions. ## Use Cases - **Customer success platforms** (Gainsight, ChurnZero) targeting mid-market companies with fragmented lifecycle data - **CDP vendors** solving the pre-sale/post-sale data unification problem - **CX analytics vendors** building on the HubSpot + Zendesk data model ================================================================================ # HubSpot Users with CRM Intent > 106,000+ HubSpot users also showing active CRM research intent — a displacement and upsell signal for companies re-evaluating their CRM and marketing automation stack. **Source:** https://gtm.ai/marketplace/hubspot-users-with-crm-intent --- ## Overview HubSpot users showing active CRM intent signals are researching CRM options, platform comparisons, or migration paths — often because they're outgrowing HubSpot or evaluating enterprise alternatives. This audience of 106,000+ companies combines the technographic confirmation of HubSpot usage with behavioral signals of active re-evaluation, making it one of the highest-quality displacement signals in the CRM market. ## What's Included Confirmed HubSpot installations paired with active CRM intent signals and composite scores. Companies span growth-stage through mid-market, reflecting HubSpot's typical customer profile plus the segment actively evaluating a move. ## Use Cases - **Salesforce, Pipedrive, and other CRM vendors** targeting HubSpot accounts showing migration intent - **RevOps consultants** identifying clients ready for CRM strategy conversations - **HubSpot services partners** finding existing customers with expansion or upgrade needs ================================================================================ # InsurTech Companies in the US > 1,700+ US insurance technology companies disrupting underwriting, claims, distribution, and customer experience **Source:** https://gtm.ai/marketplace/insurtech-companies-us --- ## Overview InsurTech covers a spectrum from full-stack digital carriers to point-solution vendors augmenting legacy insurer workflows — with fundamentally different buyer profiles, revenue models, and technology requirements. This audience covers 1,700+ US insurance technology companies across P&C, life, health, commercial, and specialty lines, with line-of-business classification, distribution model tagging, and verified executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters - **Line of Business**: P&C, life, health, commercial lines, specialty, or multi-line - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Distribution Model**: B2B (selling to carriers/MGAs), B2C (direct-to-consumer), or hybrid - **Executive Contacts**: Verified email and phone for CEO, CTO, Chief Actuary, VP of Partnerships, and VP of Sales ## Use Cases ### InsurTech Platform and API Sales Data, AI, and infrastructure vendors selling into InsurTech should use line of business and distribution model to identify the right buyer. A P&C underwriting platform has different data requirements than a digital life insurance carrier — precision targeting starts with sub-category. ### Reinsurance and Carrier Partnerships Traditional carriers and reinsurers building digital capabilities can use this audience to identify InsurTech companies pursuing carrier or MGA partnerships. Distribution model and line-of-business classification surface the most relevant co-distribution and fronting candidates. ### Regulatory and Compliance Services Insurance regulatory, actuarial, and compliance consulting firms can filter by line of business and company stage to identify InsurTech companies navigating state-by-state licensing, rate filing, and market conduct requirements — a universal pain point for digital carriers scaling distribution. ### Investor Deal Sourcing InsurTech investors can filter by line of business, funding stage, and distribution model to surface companies aligned with specific portfolio theses. Distribution model is a particularly important filter — B2B infrastructure businesses have very different unit economics and exit paths than B2C carrier plays. ================================================================================ # Intercom + HubSpot Stack > 9,200+ companies running both Intercom and HubSpot — growth-stage companies managing the full customer lifecycle from marketing acquisition through product engagement and support. **Source:** https://gtm.ai/marketplace/intercom-hubspot-stack --- ## Overview Companies running both Intercom and HubSpot have instrumented the full customer lifecycle: HubSpot captures marketing leads and manages the CRM while Intercom handles in-product messaging and support. The gap between these two systems — pre-sale versus post-sale customer data — creates a persistent pain point that customer data and lifecycle analytics vendors are built to solve. With 9,200+ qualifying companies, this is a well-defined and underserved audience. ## What's Included Confirmed dual installations of Intercom and HubSpot with company firmographics. The audience is concentrated among growth-stage SaaS and subscription businesses that manage both marketing funnels and active user bases. ## Use Cases - **CDP and customer data unification vendors** targeting the HubSpot-Intercom data gap - **Customer journey analytics platforms** selling to companies that need a unified view across acquisition and product usage - **Lifecycle marketing automation vendors** reaching companies that want to trigger HubSpot workflows from Intercom product events ================================================================================ # IT Leaders with Verified Direct Phone Numbers > 344,000+ Director and VP-level IT leaders with verified direct phone numbers — the highest-quality reachable IT contacts in ZoomInfo's database **Source:** https://gtm.ai/marketplace/it-leaders-with-direct-phone --- ## Overview This audience is filtered specifically for IT leaders with verified direct phone numbers — not switchboard lines, not corporate main numbers. At 344,000+ contacts globally, it represents the highest-quality reachable IT decision-maker layer in ZoomInfo's database. Every contact has been validated for direct dial accuracy through ZoomInfo's real-time verification engine, resulting in materially higher connect rates than standard IT contact lists. Contacts span CIOs, CTOs, VPs of IT, Directors of Infrastructure, and IT Operations leaders across industries and geographies. ## What's Included - **Identity**: Full name, verified direct phone number (mobile or direct line), and verified email address - **Role Context**: Current title and IT domain (infrastructure, security, cloud, applications, operations) - **Company Context**: Company name and employee count - **IT Environment**: Known tech stack including cloud platform, identity provider, endpoint management, and security tools - **Management Scope**: Management level and estimated team size ## Use Cases ### High-Connect-Rate IT Outbound Campaigns Standard contact lists yield 5–15% connect rates on cold calls; verified direct dials consistently outperform by 2–3x. This audience is purpose-built for outbound sequences where phone is a primary channel — SDR teams can prioritize direct dial contacts to maximize conversations per rep per day. ### IT Infrastructure and Security Sales Network infrastructure, cybersecurity, data center, and cloud migration vendors need to reach practitioners who manage the environments being sold into. IT domain filters (infrastructure, security, cloud) let you route contacts to the right product specialist or solution team. ### Managed Services and Outsourcing Outreach MSPs, IT outsourcing firms, and co-managed services providers need to reach IT decision-makers at companies where outsourcing is a viable option — typically mid-market companies with 100–1,000 employees. Employee count filters plus direct phone contact make this audience ideal for high-velocity outbound motions. ### Direct Sales Motion to Technology Decision-Makers For vendors running a direct sales motion (no SDR layer, AE-led outbound), verified direct phone combined with tech stack context enables AEs to research the account and personalize the opening call before dialing — increasing conversion from first conversation to qualified opportunity. ================================================================================ # Legal Professionals at Large Enterprises > 2,400,000+ legal department professionals at companies with 5,000+ employees — in-house counsel, legal ops, compliance, and contract management **Source:** https://gtm.ai/marketplace/legal-professionals-large-enterprises --- ## Overview Large enterprise legal departments are the primary buyers of legal technology, contract lifecycle management platforms, e-discovery tools, and outside counsel services. This audience covers 2.4M+ legal professionals at companies with 5,000+ employees — including in-house counsel, legal operations, compliance officers, and contract management teams. The enterprise threshold (5,000+ employees) ensures alignment with organizations that have dedicated legal departments with meaningful technology and services budgets. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **Title and Function**: Full title and legal function classification (general counsel, in-house counsel, legal ops, compliance, contracts, litigation, IP, regulatory) - **Company Profile**: Company name, employee count, revenue range, and public/private status - **Industry Context**: Industry classification with regulated industry flags (financial services, healthcare, pharma, energy, government) - **Legal Tech Context**: Known legal technology stack (CLM, e-discovery, matter management) where available from technographic signals - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Legal Tech and Contract Management Sales Enterprise CLM platforms, contract analytics tools, and legal workflow automation vendors target legal operations and general counsel contacts at large enterprises. Filter by industry to identify regulated sectors (financial services, healthcare, pharma) where contract volume and compliance requirements create the strongest business case. Legal ops titles are the primary evaluation lead; General Counsel is typically the economic buyer. ### E-Discovery and Litigation Support Sales E-discovery platforms, legal hold tools, and litigation support services target large enterprises with active litigation programs and in-house litigation counsel. Financial services, healthcare, and technology companies have the highest volume of active litigation matters. Use industry and title filters to identify in-house litigation teams and complement with external legal spend data where available. ### Legal Consulting and Advisory Services Law firms, boutique legal consultants, and legal process outsourcing providers use this audience to target in-house teams seeking outside counsel or specialized expertise. Large enterprise legal departments regularly evaluate outside counsel relationships — filter by industry for regulatory specialists, by title for practice area alignment (M&A, IP, employment law), and by company size for appropriate billing rate expectations. ### In-House Legal Community and CLO Outreach Legal professional associations, CLO networks, and in-house counsel communities use this audience to recruit members, distribute research, and drive event attendance. The Chief Legal Officer and General Counsel titles at large enterprises are the primary targets for executive-level community programs. Filter by industry and company revenue to build cohort-appropriate peer groups. ================================================================================ # Legal Tech Companies in the US > 2,900+ US companies building software and technology for the legal industry — from contract automation to e-discovery platforms **Source:** https://gtm.ai/marketplace/legal-tech-companies-us --- ## Overview Legal tech is a fragmented market with distinct buyer segments — law firms, corporate in-house teams, courts, and consumers — each with different procurement processes and evaluation criteria. This audience covers 2,900+ US legal technology companies building solutions across contract lifecycle management, e-discovery, legal research, practice management, and compliance, with sub-category classification and verified C-suite contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters - **Legal Tech Sub-Category**: CLM, e-discovery, legal research, practice management, compliance, or legal operations - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Target Customer**: Primary market focus — Am Law 200, mid-size law firms, in-house corporate, or government/courts - **Executive Contacts**: Verified email and phone for CEO, CTO, VP of Sales, and VP of Product ## Use Cases ### Legal Tech Platform Sales Data, AI, and infrastructure vendors targeting legal tech companies can use sub-category classification to identify the right buyer. A CLM vendor has different infrastructure requirements than an e-discovery platform — sub-category enables precise product-to-product selling. ### Law Firm Technology Sales Legal tech companies building for law firms are themselves buyers of sales tools, HR systems, and professional services. This audience identifies which legal tech vendors have law firm go-to-market motions and are therefore likely prospects for legal-adjacent software. ### In-House Legal Team Outreach Companies targeting corporate in-house legal departments can use target customer classification to identify legal tech vendors already embedded in enterprise legal teams — both as potential partners and as references for enterprise procurement conversations. ### Investor Deal Sourcing Legal tech investors can filter by sub-category, funding stage, and headcount growth to surface companies gaining traction in specific verticals. The target customer classification helps distinguish consumer-facing legal platforms from enterprise vendors — a critical distinction for fund mandate alignment. ================================================================================ # Life Sciences Companies in San Diego > 1,700+ biotech, pharmaceutical, and life sciences companies in San Diego — the third-largest biotech cluster in the United States **Source:** https://gtm.ai/marketplace/life-sciences-san-diego --- ## Overview San Diego is the third-largest biotech cluster in the United States, trailing only Boston/Cambridge and the San Francisco Bay Area. The cluster is anchored by UC San Diego, Scripps Research, the Salk Institute, and a decades-long concentration of biopharmaceutical, genomics, and medical device companies in the Torrey Pines and Sorrento Valley corridors. This audience covers 1,700+ life sciences companies in San Diego, enriched with sub-sector classification, funding stage, clinical pipeline stage where available, and academic or research institution affiliations that indicate spin-out origin or ongoing research partnerships. ## What's Included - **Company Identity**: Company name, website, and San Diego location (Torrey Pines, Sorrento Valley, downtown, La Jolla) - **Sub-Sector**: Life sciences classification (biopharmaceutical, genomics, medical device, diagnostics, digital health, CRO/CDMO, life sciences tools and reagents) - **Funding and Stage**: Funding stage, total raised, and clinical pipeline stage (preclinical, Phase I/II/III, commercial) - **Affiliations**: Academic and research institution affiliations (UCSD, Scripps, Salk, JLABS) - **Contacts**: CEO, CFO, CSO, VP R&D, VP Operations, and scientific leadership with verified email and phone ## Use Cases ### San Diego Life Sciences Platform and CRO Sales Contract research organizations, clinical trial management systems, regulatory submissions platforms, and life sciences data tools target San Diego biotech companies across the clinical development spectrum. Pipeline stage enrichment drives stage-appropriate outreach — a preclinical company needs different services than a Phase III program approaching NDA submission. ### Lab Equipment and Reagent Sales Scientific instrument vendors, lab automation providers, CRISPR and genomics reagent suppliers, and consumables distributors target San Diego's dense cluster of research-stage biotech companies. Funding recency and employee count signal purchasing capacity and procurement formality — early-stage labs buy differently than established mid-size biopharma. ### Biotech Investor Deal Sourcing Life sciences investors — venture capital, crossover funds, and family offices with biotech mandates — active in San Diego can use pipeline stage and funding data to identify companies approaching value inflection points, program milestones, or next-round fundraising. ### Scientific Recruiting and Talent Acquisition Life sciences executive search firms, academic recruiting platforms, and talent networks can use funding recency, pipeline stage, and employee growth as signals of active hiring — particularly at companies post-Series A or post-IND filing, where team-building accelerates ahead of clinical execution. ================================================================================ # Logistics and Transportation Companies > 3,000,000+ logistics, freight, and transportation companies globally with fleet data, employee count, and operations contacts **Source:** https://gtm.ai/marketplace/logistics-transportation-companies --- ## Overview Logistics and transportation is the largest industry audience in the GTM AI catalog — covering 3M+ companies globally from owner-operator trucking firms to global ocean freight forwarders. The breadth reflects the fundamental role transportation plays in every supply chain. The audience is structured by transportation mode, operational scope, and fleet size to enable precise segmentation across a market that looks homogeneous from the outside but has dramatically different buyer profiles at the sub-category level. ## What's Included - **Company Identity**: Verified name, primary domain, and headquarters location - **Transportation Mode**: Trucking (FTL/LTL), rail, air freight, ocean freight, last-mile delivery, or intermodal - **Headcount**: Employee count with 1-year growth rate - **Revenue Estimates**: Model-derived revenue ranges based on mode, fleet size, and headcount - **Fleet Profile**: Estimated fleet size and asset type where available (tractors, trailers, aircraft, vessels) - **Contacts**: Verified email and phone for VP of Operations, VP of Technology, Director of Procurement, and C-suite ## Use Cases ### Logistics Software and TMS Sales Transportation management system vendors, load planning tools, and route optimization platforms should use transportation mode and fleet size to segment their addressable market. A TMS built for LTL carriers has a fundamentally different feature set and buyer than one built for last-mile delivery operators. ### Fleet Telematics and IoT Sales ELD vendors, fleet tracking platforms, and predictive maintenance solutions can use fleet size and transportation mode to identify the highest-value targets. Trucking fleets over 50 units are the core market for most telematics solutions — fleet size filtering eliminates the long tail of owner-operators with limited budget. ### Fuel and Procurement Services Fuel card providers, fleet fuel management platforms, and procurement services can use fleet size and transportation mode to prioritize the highest-volume fuel buyers. Revenue estimates help identify which mid-size carriers have outgrown fuel management tools designed for small fleets. ### Insurance and Risk Management Commercial transportation insurers, captive managers, and risk advisory firms can use transportation mode and fleet size to target the right risk profile. Ocean and air freight carriers have dramatically different insurance requirements than regional LTL trucking companies — mode classification enables product-aligned targeting. ================================================================================ # Machine Learning Engineers and AI Engineers > 95,300+ Machine Learning Engineers, AI Engineers, MLOps, and LLM Engineers — the technical practitioners building AI systems and the primary evaluators of AI development tooling. **Source:** https://gtm.ai/marketplace/ml-and-ai-engineers --- ## Overview Machine Learning Engineers, AI Engineers, and MLOps practitioners are the hands-on builders driving AI adoption across the enterprise. They're the primary evaluators of training infrastructure, serving platforms, feature stores, and LLM tooling — and they heavily influence vendor selection even when the final signature comes from above. With 95,300+ verified records, this audience covers the full spectrum of AI engineering talent. ## What's Included Verified contacts with ML Engineer, AI Engineer, MLOps, or LLM Engineer titles. Direct contact information and company firmographics for filtering by company type (AI-native vs. enterprise adopter) and size. ## Use Cases - **MLOps and LLMOps platforms** targeting the practitioners who implement and maintain ML pipelines - **GPU cloud and compute vendors** reaching engineers who specify infrastructure requirements - **AI developer tools** (vector databases, fine-tuning platforms, model registries) reaching hands-on evaluators ================================================================================ # Machine Learning Engineers in the US > 19,200+ Machine Learning Engineers in the United States — verified contacts, tech stack, and company context **Source:** https://gtm.ai/marketplace/ml-engineers-us --- ## Overview This audience covers 19,200+ Machine Learning Engineers in the United States — practitioners building, training, and deploying production ML systems across tech, finance, healthcare, and enterprise software companies. Known ML stack data includes framework usage (PyTorch, TensorFlow, JAX), cloud platform preference (AWS, GCP, Azure), and ML-adjacent tooling across feature stores, experiment tracking, and model serving. Seniority segmentation distinguishes senior and principal-level engineers who influence architecture decisions from mid-level contributors. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and tenure at current company - **Company Data**: Company name, industry, employee count, and funding stage or revenue range - **ML Stack**: Known frameworks, cloud ML services, MLOps tooling, and data infrastructure - **Team Context**: Engineering organization size and ML team structure where available ## Use Cases ### MLOps and AI Infrastructure Sales Reach ML Engineers who evaluate and implement MLOps platforms, feature stores, and model serving infrastructure. Stack data surfaces engineers working in specific framework ecosystems where your tooling integrates natively or provides a migration path. ### Cloud Compute and GPU Platform Sales Target ML Engineers at companies with production training and inference workloads where cloud compute costs and GPU availability are active operational concerns. Company size and industry data help identify organizations with the workload scale that justifies GPU-optimized compute contracts. ### ML Tooling and Dataset Sales Identify ML Engineers at companies building models that require external datasets, labeling services, or data augmentation tooling. Industry segmentation surfaces verticals — computer vision, NLP, fraud detection — where specific dataset and annotation services are in demand. ### Technical Recruiting and Staffing Source ML Engineers in the US for production ML roles at companies building applied AI systems. Seniority and stack data enable recruiters to identify candidates with specific framework expertise and production ML experience. ================================================================================ # Manufacturing Companies in the Midwest > 426,000+ manufacturing companies across the 12-state US Midwest region with employee count, revenue estimates, and operations contacts **Source:** https://gtm.ai/marketplace/manufacturing-companies-midwest --- ## Overview The 12-state Midwest region — from Ohio to Nebraska — remains the densest concentration of manufacturing activity in the US, spanning automotive, food processing, metals, chemicals, industrial equipment, and plastics. This audience covers 426,000+ manufacturing companies in the Midwest with sub-sector classification, facility location data, revenue estimates, and verified operations and executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and headquarters state - **Manufacturing Sub-Sector**: Automotive, food and beverage, metals and fabrication, chemicals, industrial equipment, plastics, or electronics manufacturing - **Headcount**: Employee count with 1-year growth rate - **Revenue Estimates**: Model-derived revenue ranges based on headcount, sub-sector, and market data - **Facility Footprint**: HQ location and known manufacturing facility addresses by state - **Contacts**: Verified email and phone for plant managers, VP of Operations, VP of Procurement, and C-suite ## Use Cases ### Industrial Equipment and Machinery Sales Capital equipment vendors, tooling suppliers, and MRO distributors should use sub-sector classification and facility footprint to identify the right manufacturing targets. An automotive stamping plant has different equipment requirements than a food processing facility — sub-sector enables relevant, credible outreach. ### Supply Chain and Logistics Services 3PLs, freight brokers, and supply chain software vendors can use facility location data and revenue estimates to identify manufacturers with complex, multi-site distribution requirements. Companies with multiple facility locations in different states are the highest-value targets for regional logistics services. ### ERP and Manufacturing Software Sales ERP vendors, MES platforms, and quality management systems should use revenue estimates and employee count to segment the market by implementation scope. A 50-person job shop and a 2,000-person tier-1 supplier have entirely different ERP requirements and budget cycles. ### Workforce and Staffing Services Manufacturing is the highest-volume segment for industrial staffing. Use sub-sector and headcount growth rate to identify which manufacturers are actively expanding production capacity and most urgently need skilled trades, assembly, and quality control staff. ================================================================================ # Marketing Leaders at E-Commerce Companies > 165,000+ Director and VP-level marketing leaders at retail and e-commerce companies — verified contacts, channel mix, and MarTech stack **Source:** https://gtm.ai/marketplace/marketing-leaders-ecommerce --- ## Overview This audience targets Director and VP-level marketing leaders at e-commerce and retail companies globally. Contacts are enriched with known MarTech and adtech stack — including email service providers, paid media platforms, CDPs, and personalization tools — enabling competitors to identify displacement opportunities and complementary vendors to find their natural adjacencies. Coverage spans pure-play e-commerce companies as well as brick-and-mortar retailers with significant digital commerce operations. ## What's Included - **Identity**: Full name, verified email, direct phone, and LinkedIn profile URL - **Role Context**: Current title and seniority level (Director, VP, SVP, CMO), with functional specialization (performance marketing, brand, CRM, growth) - **Company Context**: Company name and e-commerce category (apparel, beauty, home goods, electronics, marketplace, DTC) - **MarTech Stack**: Known tools across ESP, CDP, personalization, analytics, paid media, and attribution categories - **Company Size**: Revenue range and estimated customer base size ## Use Cases ### E-Commerce MarTech and Personalization Sales Personalization engines, product recommendation platforms, and A/B testing tools target Director and VP-level marketers who own on-site conversion optimization. MarTech stack enrichment identifies accounts already running specific tools, enabling direct competitive displacement or integration-led messaging. ### Digital Advertising and Media Sales Paid media platforms, retail media networks, and programmatic advertising vendors can segment by revenue range and e-commerce category to prioritize accounts with the ad spend scale to justify enterprise media partnerships. ### Customer Data Platform and CDP Sales CDPs and customer identity platforms target growth-stage and enterprise e-commerce companies managing fragmented first-party data across channels. MarTech stack data identifies companies still running on legacy data infrastructure with high displacement potential. ### Agency and Creative Services Digital agencies, performance marketing firms, and creative studios targeting e-commerce marketing leaders can use e-commerce category and company revenue to identify accounts with the scale and channel complexity that justify agency relationships. ================================================================================ # Marketing Operations Leaders > VP, Director, and Head of Marketing Operations professionals — the architects of the marketing tech stack, campaign infrastructure, and data governance behind the marketing function. **Source:** https://gtm.ai/marketplace/marketing-operations-leaders --- ## Overview Marketing Operations leaders are the behind-the-scenes architects of the marketing technology stack — they're responsible for MAP configuration, attribution infrastructure, lead routing, and data quality. They're often the most influential evaluators for martech purchases, even when the CMO signs the contract. With 25,000+ verified Marketing Ops professionals, this audience targets the true decision-making layer in marketing technology procurement. ## What's Included Verified VP, Director, and Head of Marketing Operations contacts. Direct email and phone with company firmographics for targeting by company size and industry. ## Use Cases - **Marketing automation and MAP vendors** reaching the admins and architects who run their platforms - **Data quality and enrichment vendors** targeting Marketing Ops as the gatekeepers of clean CRM data - **Attribution and analytics platforms** selling to the professionals responsible for marketing measurement ================================================================================ # Marketing VPs at Software Companies > A targeted audience of VP Marketing and Head of Marketing leaders at software companies — the most active buyers of B2B marketing technology and demand generation tools. **Source:** https://gtm.ai/marketplace/marketing-vps-at-software-companies --- ## Overview VP Marketing and Head of Marketing leaders at software companies represent the most active and sophisticated segment of the B2B marketing technology market. They're running complex demand generation programs, managing multi-channel stacks, and deeply familiar with the tools being sold to them — which means they evaluate quickly and buy based on differentiated value. This audience targets that precise combination of seniority, function, and industry. ## What's Included VP-level marketing contacts at companies classified in the software industry, including direct email, phone, and company firmographics. The audience spans software companies across all sizes — from growth-stage SaaS to large enterprise software vendors. ## Use Cases - **Martech platforms** targeting the most analytically fluent marketing buyers in the market - **Demand generation and ABM tools** reaching marketing leaders with defined pipeline responsibilities - **Marketing analytics and attribution vendors** selling to VPs who own revenue marketing metrics ================================================================================ # MarTech Companies in the US > 6,800+ US marketing technology companies with tech stack, funding data, and key decision-maker contacts **Source:** https://gtm.ai/marketplace/martech-companies-us --- ## Overview The US MarTech landscape includes over 6,800 companies spanning email marketing, SEO, analytics, customer data platforms, ad tech, and content management — each with its own buyer set, integration dependencies, and competitive dynamics. This audience provides the full MarTech company universe with category classification, detected integration ecosystem, funding data, and verified VP and C-suite contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters - **MarTech Category**: Email, SEO/SEM, analytics, CDP, ad tech, content, social media, attribution, or marketing automation - **Funding Profile**: Stage, total raised, and most recent round date - **Headcount**: Employee count with 1-year growth rate - **Integration Ecosystem**: Detected integrations with major platforms (Salesforce, HubSpot, Snowflake, Google, etc.) - **Executive Contacts**: Verified email and phone for CEO, CMO, CTO, VP of Product, and VP of Partnerships ## Use Cases ### MarTech Platform and Data Sales Data enrichment, identity resolution, and AI infrastructure vendors selling to MarTech companies can use category classification and integration ecosystem data to identify which companies have compatible data architectures and are most likely to evaluate complementary solutions. ### Agency and Partner Channel Development Digital marketing agencies and systems integrators can use this audience to identify MarTech vendors whose products align with their client delivery capabilities. Integration ecosystem data surfaces companies whose tech stack is already compatible with the agency's existing practice. ### Competitive Displacement Campaigns Use MarTech category and funding stage to identify competitors' customers — companies in the same category with similar funding profiles and integration stacks who are at natural evaluation points. Layer in headcount growth to prioritize accounts actively scaling their marketing operations. ### Investor Deal Sourcing MarTech investors can filter by category, funding stage, and integration ecosystem to identify companies gaining traction in high-priority segments. Integration data is a strong signal of market adoption — companies with deep integrations into major platforms are harder to displace and more defensible. ================================================================================ # Media and Entertainment Companies in Los Angeles > 165,000+ media, entertainment, and creative technology companies in the Los Angeles metro area **Source:** https://gtm.ai/marketplace/media-entertainment-la --- ## Overview Los Angeles is the global center of media and entertainment, housing the world's largest concentration of film studios, television networks, music labels, gaming companies, and streaming platforms. This audience covers 165,000+ companies across the full media and entertainment spectrum — from major studios and streaming giants to independent production companies, music publishers, gaming studios, talent agencies, and creative technology companies. Contacts are enriched with sub-sector classification, known production and technology stack, and executive leadership contacts across both creative and business functions. ## What's Included - **Company Identity**: Company name, website, and LA area location - **Sub-Sector**: Media and entertainment classification (film/TV production, streaming, music, gaming, talent/agencies, post-production, AdTech/MediaTech, animation) - **Size**: Employee count and revenue range - **Tech Stack**: Known production, distribution, and technology infrastructure (streaming platforms, production tools, distribution systems, ad serving) - **Ownership**: Studio-owned, independent, PE-backed, or publicly traded - **Contacts**: CEO, CFO, CTO, CPO, and VP-level contacts with verified email and phone ## Use Cases ### Media Technology and Production Tool Sales Production software, visual effects tools, cloud rendering platforms, digital asset management systems, and post-production infrastructure vendors target LA's dense cluster of studios, production companies, and post-production facilities. Sub-sector and company size filters identify accounts at the scale where enterprise production tool purchases are made vs. individual creative software licenses. ### Streaming and Content Platform Sales Content discovery, audience analytics, ad insertion, and streaming infrastructure vendors target the technology and product leadership at streaming platforms and SVOD/AVOD services concentrated in LA. Technology stack enrichment identifies accounts running on incumbent platforms vs. building proprietary infrastructure — different sales motions for each. ### LA Entertainment Industry Outreach Professional services firms — law firms, accounting firms, talent representation, insurance brokers — serving the entertainment industry can use sub-sector and company size to build precise client targeting lists within the entertainment ecosystem, where relationships and industry-specific expertise are primary buying criteria. ### Creative and Digital Agency Services Digital agencies, social media marketing firms, and creative studios targeting entertainment brands can use ownership type and revenue filters to identify companies with marketing budgets at the scale that justifies agency relationships — distinguishing major studio campaigns from independent production marketing. ================================================================================ # New C-Suite Hires in the Last 30 Days > 4,100+ C-suite executives who started a new role in the last 30 days — the freshest executive signal in ZoomInfo's database **Source:** https://gtm.ai/marketplace/new-c-suite-hires-30-days --- ## Overview The 30-day C-suite signal is the most time-sensitive executive audience in ZoomInfo's database. These are executives who started their roles within the last calendar month — before vendor relationships are re-established, before budgets are locked, and before the executive's calendar fills up. This audience is refreshed daily and is purpose-built for outreach programs that prioritize recency over volume. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Role Details**: C-suite title (CEO, CFO, COO, CTO, CMO, CISO, CRO, CPO, etc.), company name, and precise start date - **Previous Background**: Prior employer and title for context-driven outreach personalization - **Company Context**: Employer revenue tier, employee count, and industry classification - **Seniority Tier**: Function-level C-suite categorization for targeted sequences by buyer role - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Day-One Executive Outreach Programs The first 30 days are the optimal window for vendor outreach to new executives — response rates decline measurably after 90 days as the executive settles in and incumbents re-establish relationships. Day-one programs combine this signal with personalized messaging referencing the executive's prior company and known challenges to establish contact before the competition does. ### Sales Tool and Platform Sales to New CXOs New CXOs evaluate vendor relationships as part of their first-quarter reset. A new CFO reviewing the financial tech stack, a new CMO auditing the marketing platform, or a new CRO resetting the revenue stack — all represent open evaluation cycles. Use C-suite function to route records to the right product team and trigger outreach within 48 hours of the job change signal. ### Executive Gifting and Relationship Programs High-touch executive relationship programs — gifting, curated research, and invitation-only events — use this signal to identify the right moment for a first touchpoint. A well-timed, thoughtful outreach in the first 30 days can establish a relationship that persists for the executive's entire tenure. Filter by company size and industry to match your ideal customer profile. ### ABM Campaigns Targeting Newly Placed Budget Owners ABM programs that trigger on C-suite changes at target accounts see higher win rates than static account-based sequences. A new CRO, CMO, or CFO at a named account is a strong signal to activate a full account-based sequence — coordinating outreach across multiple stakeholders timed to the executive transition. ================================================================================ # New C-Suite Hires in the Last 90 Days > 70,900+ C-suite executives who started a new role in the last 90 days — the highest-intent executive audience for outreach, updated daily **Source:** https://gtm.ai/marketplace/new-c-suite-hires-90-days --- ## Overview New executives are in their window of maximum influence — they're evaluating vendors, resetting budgets, and building their teams. This audience captures C-suite leaders within their first 90 days, when outreach is most likely to land. Records are updated daily from job change signals across LinkedIn, company announcements, and press releases. The 90-day window is the standard benchmark for executive transition outreach — long enough to capture most new hires, short enough that most are still in evaluation mode. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Role Details**: Title, company, and position start date - **Previous Background**: Prior company, title, and tenure — useful for personalization and competitive intelligence - **Company Context**: Current employer size, industry, and revenue range - **Seniority Classification**: C-suite tier (CEO, CFO, COO, CTO, CMO, CISO, CRO, CPO, etc.) - **Verified Contact Data**: Work email and direct phone number with confidence scoring ## Use Cases ### New Executive Onboarding Outreach The first 90 days are when new executives are most open to vendor conversations — they haven't yet committed to incumbents, they're benchmarking the market, and they're actively building relationships. Personalize outreach using the executive's previous company and role to establish credibility and relevance before the evaluation window closes. ### Sales Technology and Tool Sales to New Leaders New sales, marketing, and operations executives frequently reset their technology stack in the first quarter. This is the primary driver of competitive displacement in enterprise SaaS — the new CRO who came from Salesforce shops, or the new CMO who rebuilt demand gen at their last company. Use previous employer data to anticipate technology preferences and position accordingly. ### Executive Advisory and Consulting Services Strategy consultants, executive coaches, and fractional advisors target new executives who are establishing their priorities and building their advisory network. Outreach within the first 90 days — before the executive's calendar is fully committed — significantly improves response rates. Filter by industry and company size to align with your advisory firm's focus areas. ### Recruiting and Headhunting for Competitive Intelligence Executive search firms and in-house talent teams use new hire data to track competitor moves, identify recently displaced executives from companies undergoing leadership changes, and map talent flows across industries. The previous company and role fields are critical inputs for competitive talent intelligence programs. ================================================================================ # New CFOs in the Last 90 Days > 4,700+ Chief Financial Officers who started a new role in the last 90 days — the most budget-influential executive hire signal in B2B **Source:** https://gtm.ai/marketplace/new-cfos-90-days --- ## Overview The CFO controls the budget approval process for every major vendor relationship in the company. New CFOs are the single highest-value executive hire signal for any vendor selling into finance — ERP and financial planning software, audit and advisory services, banking relationships, and financial infrastructure all reset when a CFO changes. This audience captures 4,700+ new CFOs globally in the last 90 days, updated daily from verified job change signals. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Role Details**: CFO title, company name, and exact position start date - **Previous Background**: Prior employer and title — valuable for personalization and understanding finance philosophy - **Company Financial Profile**: Revenue tier, employee count, and publicly available financial context (public vs. private, PE-backed, VC-backed) - **Industry Classification**: For compliance, regulatory, and industry-specific financial context - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Financial Software and ERP Sales to New CFOs New CFOs consistently review and often replace financial systems in their first two quarters — ERP platforms, financial planning and analysis tools, close management software, and expense management systems are all on the evaluation list. The 90-day window captures CFOs while they're still in the assessment phase, before incumbent vendors have re-established relationships and before the annual budgeting cycle locks in commitments. ### Audit and Advisory Services Public accounting firms, internal audit consultants, and financial advisory services target new CFOs who are establishing their external advisor relationships. The Big 4 and mid-market advisory firms actively track CFO transitions to re-bid audit engagements and position advisory services before the prior firm re-signs. Filter by company revenue tier to align with your firm's target client size. ### Banking and Credit Relationship Development Commercial bankers, credit officers, and treasury management teams track CFO transitions as a primary trigger for relationship outreach. A new CFO represents an opportunity to re-open banking relationships — credit lines, treasury services, and capital markets relationships all get reviewed when financial leadership changes. Target by company revenue and industry for relationship team assignment. ### CFO Peer Community and Advisory Outreach CFO peer groups, executive networks, and advisory boards actively recruit new CFOs in their first 90 days — before the executive's schedule fills up and while they're actively seeking peer relationships. Filter by company size and industry to match your community's focus and maximize acceptance rates for invitations and introductions. ================================================================================ # New CISOs in the Last 90 Days > 527+ Chief Information Security Officers who started a new role in the last 90 days — new security leaders evaluating their entire stack **Source:** https://gtm.ai/marketplace/new-cisos-90-days --- ## Overview CISO transitions are among the highest-value signals in cybersecurity sales. New CISOs conduct full security stack reviews in their first 90 days — assessing current vendors, identifying gaps, and establishing their security roadmap. This audience is intentionally narrow (527+ records) because CISO-level roles are rare, high-value, and the signal quality justifies focused, high-investment outreach programs. Updated daily from verified job change signals across LinkedIn and press announcements. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Role Details**: CISO title and variants (Chief Information Security Officer, VP of Information Security, Head of Security), company, and start date - **Previous Security Background**: Prior company and role — indicates security philosophy, known vendor relationships, and areas of expertise - **Company Profile**: Employee count, revenue tier, industry classification, and regulated industry flags (financial services, healthcare, critical infrastructure) - **Compliance Context**: Known compliance frameworks relevant to the company's industry (SOC 2, HIPAA, PCI, FedRAMP, etc.) - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Security Platform Sales to New CISOs New CISOs conduct 30/60/90-day reviews of their security program — and vendors that reach them in the first 30 days can shape the evaluation criteria before the formal RFP process begins. Use previous employer data to understand the CISO's prior stack and identify whether they're likely to consolidate platforms or expand best-of-breed coverage. Regulated industry flags help prioritize the highest-value targets for enterprise security platforms. ### MSSP and Managed Security Services Outreach Many organizations appoint a CISO for the first time after a security incident or compliance mandate — and the new CISO often determines quickly that they need external SOC support. MSSPs and managed detection and response vendors should target first-time CISO appointments at mid-market companies (500–5,000 employees) where internal security headcount is limited. ### Security Assessment and Consulting Services New CISOs frequently engage external consultants for security program assessments, penetration testing, red team exercises, and gap analyses in their first quarter — establishing a baseline before committing to vendor investments. Security consulting firms and boutique advisory practices should lead with assessment offerings rather than product sales to match the CISO's immediate need. ### CISO Peer Community and Advisory Programs CISO networks and peer communities — both vendor-sponsored and independent — actively recruit new CISOs in their first 90 days. Security leaders new to a role actively seek peer relationships for benchmarking, threat intelligence sharing, and vendor validation. Invitations to CISO roundtables, advisory boards, and peer research programs see high acceptance rates in this window. ================================================================================ # New CMOs in the Last 90 Days > 1,100+ Chief Marketing Officers who started a new role in the last 90 days — actively resetting agency relationships, tech stack, and budgets **Source:** https://gtm.ai/marketplace/new-cmos-90-days --- ## Overview CMO transitions are the primary trigger for MarTech stack resets, agency reviews, and brand strategy overhauls. New CMOs bring their prior technology preferences, agency relationships, and strategic frameworks — creating both displacement risk for incumbents and opportunity for vendors who can align with the new CMO's playbook. This audience covers 1,100+ new CMOs globally in the last 90 days, updated daily. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Role Details**: CMO title and variants (Chief Marketing Officer, SVP Marketing, Head of Marketing), company name, and position start date - **Previous Background**: Prior employer and role — critical for predicting technology preferences and agency relationships - **Company Profile**: Revenue tier, employee count, industry classification, and B2B vs. B2C designation - **Marketing Org Context**: Company's known marketing technology stack and digital maturity signals where available - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### MarTech Platform Sales to New CMOs New CMOs evaluate their marketing technology stack as one of their first priorities — MAP platforms, CDP, intent data, ABM tooling, and attribution software all get reviewed. The previous employer data is your best signal for what technology they've used before and what gaps they're likely to want to fill. Personalize outreach around the CMO's prior company's marketing approach and position your platform as the upgrade they've been waiting to make. ### Agency and Creative Services Outreach Creative agencies, PR firms, demand generation agencies, and media buying firms all track CMO transitions as a primary new business trigger. New CMOs frequently replace incumbent agencies in their first two quarters — especially if the previous CMO had long-standing agency relationships. Outreach that arrives in the first 30 days, before the agency review is formally initiated, has the best chance of getting on the shortlist. ### Demand Gen and Brand Consulting Strategy and marketing consulting firms target new CMOs who are establishing their go-to-market strategy, brand positioning, and demand generation framework. Early-stage consulting engagements — audits, strategy workshops, and positioning reviews — are most likely to be approved before the CMO's budget is committed to ongoing programs. Filter by company revenue and stage to align with your consulting firm's target profile. ### CMO Peer Community and Thought Leadership CMO networks, executive roundtables, and marketing thought leadership programs use new CMO data to time invitations and content outreach. New CMOs are actively building their peer network in the first 90 days — introductions to relevant peer communities, curated research, and invitation-only events are received positively during this window before the executive's schedule is fully committed. ================================================================================ # New Director Hires in the Last 30 Days > 7,800+ Director-level leaders who started a new role in the last 30 days — fresh mandates and active vendor evaluation **Source:** https://gtm.ai/marketplace/new-director-hires-30-days --- ## Overview Directors are the operational buyers in most organizations — they manage vendor relationships, run evaluations, and own day-to-day tool decisions even when budget approval sits above them. New Director hires in the last 30 days represent the freshest possible signal: they've just arrived, haven't committed to incumbents, and are actively building their workflows and tool set. This audience is updated daily with verified job change signals. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Role Details**: Full Director title and function, company name, and position start date - **Previous Background**: Prior company and role for outreach context and personalization - **Company Context**: Employer size, industry classification, and revenue range - **Functional Scope**: Director of Marketing, Director of Engineering, Director of Finance, Director of Operations, etc. - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Director-Level Onboarding Outreach The 30-day window is the tightest and highest-intent signal in job change data — these leaders have just arrived and haven't yet committed to vendor relationships. Outreach that references their new role and company-specific context (team size, growth trajectory, competitive landscape) lands meaningfully better than generic outreach at this window. ### Department Software and Tooling Sales Directors are the hands-on evaluators for department-level software. A new Director of Demand Generation will audit their marketing automation and intent data subscriptions in week one. A new Director of Engineering will review their CI/CD and observability stack. Use functional title to match your product to the right Director profile and reach them before the evaluation is complete. ### Consulting and Advisory Services for New Leaders Boutique consulting firms, fractional executives, and functional advisors target new Directors who are building their operating model. The 30-day signal is particularly useful for advisors who can offer onboarding support, quick assessments, or peer introductions — services that are most valuable in the first month of a new role. ### Event and Community Outreach to Emerging Leaders Director-level professionals are the primary audience for industry events, community platforms, and professional development programs. New Director hires are at a career inflection point — actively building their professional network and seeking peer connections. Use this audience to drive event registrations, community sign-ups, and thought leadership content distribution to an audience primed to engage. ================================================================================ # New Engineering Directors (Last 60 Days) > 4,200+ Directors of Engineering who started a new role within the past 60 days — a strong signal for developer tools, hiring platforms, and engineering management software. **Source:** https://gtm.ai/marketplace/new-engineering-directors-last-60-days --- ## Overview Directors of Engineering entering a new role are in a critical evaluation window: they're setting team structure, tooling standards, and hiring plans. This audience captures 4,200+ Directors of Engineering verified to have started their current position within the past 60 days, refreshed daily to maintain timing precision. ## What's Included Each record includes the director's verified start date, current employer, job function, and direct contact details. The audience spans companies of all sizes, with strong representation across software, fintech, and cloud-native businesses. ## Use Cases - **Developer tools** (IDEs, CI/CD, code review, observability) targeting engineering leaders who set standards - **Technical recruiting and staffing firms** reaching directors in active hiring mode - **Engineering management platforms** (1-on-1 tools, performance, OKR) pitching to newly appointed managers ================================================================================ # New Sales Leaders at SaaS Companies (Last 60 Days) > 327+ new VP of Sales, Head of Sales, and CRO hires at SaaS companies in the last 60 days — the highest-intent signal for sales tool vendors **Source:** https://gtm.ai/marketplace/new-sales-leaders-saas-60-days --- ## Overview New sales leaders at SaaS companies are the single most reliable buying signal for sales technology vendors. VPs of Sales, Heads of Sales, and CROs who join a new company within the last 60 days are actively evaluating their CRM, sales engagement stack, forecasting tools, and enablement platform — often before their first full quarter of results. This audience is intentionally narrow (327+ records) to represent genuinely high-fit, high-intent targets rather than a broad contact list. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Role Details**: Full title (VP of Sales, CRO, Head of Sales, Chief Revenue Officer), company name, and start date - **Sales Background**: Prior company, title, and sales org context — indicates preferred technology and methodology - **Company Context**: SaaS classification, ARR range estimates, employee count, and sales team size signals - **Funding Stage**: Seed, Series A/B/C, growth, or public — useful for aligning your platform's target segment - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Sales Engagement and Enablement Tool Sales New SaaS sales leaders review their sales engagement stack in the first 60 days — sequencing tools, call recording, conversation intelligence, and playbook platforms all get evaluated. Use the previous company field to identify leaders who came from companies known to use your platform (or your competitors') and personalize outreach accordingly. The 60-day window is tight enough that most leaders haven't yet committed to renewals. ### CRM and Forecasting Platform Sales New sales leaders at SaaS companies are the primary driver of CRM consolidations, migrations, and forecasting platform upgrades. A new CRO who came from a Salesforce shop joining a HubSpot company will likely evaluate a migration. Forecasting and pipeline inspection tools are almost always on the 90-day review list. Reach these leaders in their first 60 days with a credible ROI story before their Q1 budget allocation is finalized. ### Sales Recruiting and Headhunting Executive search firms and in-house talent teams track new sales leader appointments to map competitive talent flows and identify companies building out their sales organization. A new VP of Sales at a Series B SaaS company signals an impending GTM build-out — often 10–30 new sales hires in the following 6 months. Use this audience to get ahead of the hiring wave. ### RevOps Consulting for New Sales Leadership RevOps consultants, fractional CROs, and GTM advisory firms target new sales leaders who are auditing their revenue operations infrastructure. The first 60 days is when new leaders identify gaps in their tech stack, data quality, and process — creating demand for advisory engagements before they commit to specific tool purchases. Filter by ARR range and company stage to match your advisory firm's ideal client profile. ================================================================================ # New VP Hires in the Last 90 Days > 39,500+ VP-level executives who started a new role in the last 90 days — actively building teams and evaluating tools in their first quarter **Source:** https://gtm.ai/marketplace/new-vp-hires-90-days --- ## Overview VP-level hires represent a high-volume, high-intent signal — they're budget owners with new mandates, actively building teams and selecting tools in their first quarter. This audience covers 39,500+ new VP hires across all functions globally, updated daily. VP-level executives move faster than C-suite in vendor evaluation and are often the primary economic buyer for department-level software and services purchases. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Role Details**: Full VP title (including function), company, and position start date - **Previous Background**: Prior company, title, and tenure — key for outreach personalization - **Company Context**: Employer size, industry classification, and revenue range - **Functional Classification**: VP of Sales, VP of Marketing, VP of Engineering, VP of Finance, VP of Operations, etc. - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### New VP Outreach for Sales and Marketing Tools VPs of Sales and Marketing are the primary buyers of CRM, sales engagement, marketing automation, and demand generation tools. New VP hires in these functions are evaluating their entire tech stack in the first 90 days. Use functional title filtering to target the right VP profile for your product category and lead with insights from their previous company's approach to the problem you solve. ### Executive Advisory and Consulting Strategy, operations, and functional consulting firms target new VP hires who are establishing their team structure, OKRs, and operating model. Outreach within the first 60 days — before the VP has built out their advisory network — delivers significantly higher response rates than cold outreach to tenured executives. ### Recruiting and Talent Pipeline Development In-house recruiters and executive search firms track VP-level transitions to identify recently available talent, map competitive team structures, and understand where top performers are landing. The previous employer field enables competitive talent intelligence — tracking VP-level exits from major companies as they move to new roles. ### ABM Campaigns Targeting New Budget Owners ABM programs that target accounts based on known executive changes see measurably higher pipeline conversion. A new VP of Marketing at a target account represents a re-opened budget conversation. Use this audience to trigger ABM sequences at accounts with fresh VP hires — especially in functions directly relevant to your product's buyer profile. ================================================================================ # New VP Marketing Hires (Last 90 Days) > 2,900+ VP-level marketing leaders who started a new role within the past 90 days — a prime buying window for martech vendors and agencies before the new VP's stack decisions are locked. **Source:** https://gtm.ai/marketplace/new-vp-marketing-last-90-days --- ## Overview A new VP of Marketing is evaluating everything — agencies, tools, channels, and measurement frameworks — within their first 90 days. They arrive with fresh perspectives and often with mandates to change what isn't working. This audience captures 2,900+ VP-level marketing leaders verified to have started their current role within the past 90 days, updated daily as new appointments are detected. ## What's Included VP-level marketing contacts with confirmed position start dates within the past 90 days. Direct email and phone with company firmographics for prioritizing by company size and industry. ## Use Cases - **Martech stack vendors** (MAP, ABM, attribution) reaching VPs before their technology review is complete - **Demand gen and content agencies** pitching to new VPs rebuilding their program and agency roster - **Marketing analytics and attribution vendors** targeting leaders who are resetting their measurement foundation ================================================================================ # New VP of Sales Hires (Last 90 Days) > 3,100+ VP-level sales leaders who started a new role within the past 90 days — the highest-intent window for evaluating new sales tools and methodologies. **Source:** https://gtm.ai/marketplace/new-vp-sales-last-90-days --- ## Overview A new VP of Sales is one of the most predictable buying triggers in B2B. Within their first 90 days, they're evaluating CRM configuration, sales engagement tools, compensation software, and enablement platforms. This audience captures 3,100+ VP-level sales leaders who recently started a new role — identified through position start date signals updated daily. ## What's Included Each record includes the contact's verified start date at their current company, role title, employer details, and direct contact information. The audience refreshes daily as new hires are detected, keeping outreach windows accurate and timely. ## Use Cases - **Sales tech vendors** (CRM, engagement, compensation, forecasting) reaching new sales leaders before incumbents renew - **Executive coaches and recruiting firms** working with VP Sales transitions - **CFOs and COOs** selling financial planning tools to companies with new revenue leadership ================================================================================ # Operations Leaders at Logistics Companies > 75,300+ Director and VP-level operations leaders at logistics, freight, and transportation companies — verified contacts and operational context **Source:** https://gtm.ai/marketplace/operations-leaders-logistics --- ## Overview This audience covers Director and VP-level operations leaders at logistics, freight, transportation, and third-party logistics (3PL) companies globally. Contacts are enriched with logistics category (trucking, rail, ocean, air freight, last-mile, warehousing), fleet or network size estimates, and known TMS and operations technology stack — giving vendors the context to segment by operational complexity and identify accounts running on legacy infrastructure. Coverage spans asset-based carriers, 3PLs, freight brokers, and logistics technology companies. ## What's Included - **Identity**: Full name, verified email, direct phone, and LinkedIn profile URL - **Role Context**: Current title, operational scope (national, regional, last-mile, cross-border), and functional area (fleet ops, warehouse ops, dispatch, supply chain) - **Company Context**: Company name and logistics category (trucking, 3PL, freight brokerage, ocean/air freight, last-mile) - **Operational Scale**: Fleet size or warehouse network estimates where available - **Tech Stack**: Known TMS, WMS, ELD, and route optimization platforms in use ## Use Cases ### Logistics Software and TMS Sales Transportation management system vendors can use logistics category and fleet size filters to prioritize accounts by operational complexity and deal potential. TMS stack enrichment identifies accounts running on legacy systems like older Oracle TMS or homegrown tools — classic displacement targets. ### Fleet Management and Telematics Sales Fleet tracking, ELD compliance, and driver safety platforms target VP-level fleet operations leaders at asset-based carriers. Fleet size estimates allow reps to segment by vehicle count and price accordingly — small fleets (5–50) vs. large carrier networks (500+) require different product tiers and sales motions. ### Warehouse Automation and Robotics Warehouse automation, conveyor systems, and AMR/AGV vendors target operations and warehouse VPs at 3PLs and distribution-intensive shippers. Company size and warehouse network data identifies accounts at the scale threshold where automation ROI becomes compelling. ### Insurance and Risk Management Services Commercial fleet insurance, cargo insurance, and supply chain risk platforms target operations and risk leaders at logistics companies. Fleet size and logistics category are strong proxies for risk profile and policy value. ================================================================================ # PE-Backed Companies in North America > 11,000+ private equity-backed companies in the US and Canada with sponsor details, portfolio context, and executive contacts **Source:** https://gtm.ai/marketplace/pe-backed-companies-north-america --- ## Overview Private equity-backed companies operate under a distinct set of priorities — value creation timelines, EBITDA targets, and portfolio-wide technology standardization mandates that create concentrated buying opportunities for the right vendors. This audience covers 11,000+ PE-backed companies in the US and Canada with sponsor identification, platform vs. add-on classification, revenue estimates, and verified C-suite and VP-level contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and North American headquarters - **PE Sponsor**: Fund name, managing firm, and known investment date - **Portfolio Classification**: Platform company vs. add-on/bolt-on acquisition designation - **Headcount**: Employee count with 1-year growth rate - **Revenue Estimates**: Model-derived revenue ranges based on industry, headcount, and market data - **Executive Contacts**: Verified email and phone for CEO, CFO, CTO, VP of Operations, and VP of Sales ## Use Cases ### PE Portfolio Company Sales PE-backed platform companies are among the most efficient enterprise sales targets — they have investment mandates to scale, defined value creation timelines, and often PE-sponsor-level relationships that can accelerate deal cycles. Use sponsor identification to map your existing PE relationships to portfolio companies that should already be warm. ### M&A Integration Services PE firms executing buy-and-build strategies need integration services after every add-on acquisition. IT integration, financial consolidation, HR systems harmonization, and operational consulting firms should use platform company classification and acquisition cadence to identify the highest-volume buyers of integration services. ### Financial and Operational Consulting CFO advisory, FP&A outsourcing, and operational improvement firms should use revenue estimates and industry classification to identify PE-backed companies in the value creation phase — typically the 12–36 months post-acquisition when operational initiatives are most actively funded. ### PE-Focused Software and Services Sales ERP, CRM, HR, and financial reporting vendors that have built PE-specific product features or pricing should use this audience to reach the full universe of relevant accounts. Sponsor identification allows reps to reference mutual relationships and tailor messaging to the PE ownership context — a significant conversion advantage over cold outreach. ================================================================================ # PE-Backed Companies with Cybersecurity Intent > 8,400+ private equity-backed companies showing active cybersecurity research signals — organizations with PE-mandated security requirements and the budget to act on them. **Source:** https://gtm.ai/marketplace/pe-backed-companies-cybersecurity-intent --- ## Overview Private equity-backed companies face unique cybersecurity pressures: PE sponsors typically require security audits, SOC 2 compliance, and risk assessments as conditions of investment or in preparation for exit. When these companies also show active cybersecurity research intent, the combination creates a high-priority, high-budget buying signal. These 8,400+ companies are prime targets for security vendors and MSSPs. ## What's Included Records combine PE funding confirmation with active cybersecurity intent signals and firmographics. Companies in this audience have both the strategic pressure (PE requirements) and the behavioral signal (active research) that indicate near-term purchasing activity. ## Use Cases - **Cybersecurity and compliance vendors** targeting PE portfolio companies under investor security scrutiny - **MSSPs** offering managed security services to PE-backed companies preparing for audit or exit - **GRC platforms** reaching organizations with PE-driven compliance mandates ================================================================================ # Pharmaceutical Companies in the US > 68,000+ US pharmaceutical companies with pipeline focus, revenue data, employee count, and executive contacts **Source:** https://gtm.ai/marketplace/pharmaceutical-companies-us --- ## Overview The US pharmaceutical market spans global branded drug manufacturers, generic producers, biotechs with active clinical pipelines, and contract research and manufacturing organizations — each operating under distinct regulatory frameworks and procurement structures. This audience covers 68,000+ US pharmaceutical companies with sub-category classification, therapeutic area focus, funding data, and verified executive and scientific leadership contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters - **Pharma Category**: Branded, generic, biotech, specialty pharma, CRO, or CDMO - **Financials**: Revenue estimates and employee count - **Funding Profile**: Stage, total raised, and most recent round date (for private companies) - **Therapeutic Area**: Oncology, immunology, CNS, cardiovascular, rare disease, or other therapeutic focus where available - **Leadership Contacts**: Verified email and phone for CEO, CFO, Chief Medical Officer, VP of R&D, and VP of Clinical Operations ## Use Cases ### Pharma Technology and Software Sales Clinical data management, regulatory affairs, ERP, and supply chain software vendors should use pharma category and company size to identify the right buyer and positioning. A global branded pharma company and a clinical-stage biotech have entirely different software maturity levels and procurement processes. ### Clinical Research and Outsourcing Services CROs, site management organizations, and clinical technology vendors can use therapeutic area and funding stage to identify pharma companies with active or near-term clinical development programs. Companies with recent funding and early-stage pipelines represent the highest-volume buyers of outsourced research services. ### Regulatory and Compliance Solutions Regulatory consultancies, pharmacovigilance platforms, and quality management systems can filter by pharma category to identify companies navigating specific FDA pathways and compliance requirements. Branded and specialty pharma companies have significantly more complex regulatory needs than generic manufacturers. ### Investor Deal Sourcing Life sciences investors can filter by therapeutic area, funding stage, and pharma category to surface companies that fit specific portfolio mandates. Therapeutic area classification is essential for fund specialization — an oncology-focused fund and a rare disease fund are evaluating entirely different sets of companies. ================================================================================ # Procurement and Vendor Management Leaders > 20,100+ VP and Director-level Procurement and Purchasing leaders — the gatekeepers for enterprise vendor selection, contract negotiation, and spend management. **Source:** https://gtm.ai/marketplace/procurement-and-vendor-leaders --- ## Overview Procurement and vendor management leaders control enterprise purchasing — they evaluate vendors, negotiate contracts, and enforce spend policies across the organization. Any software vendor selling to enterprises will eventually intersect with procurement. With 20,100+ verified procurement leaders, this audience is essential for both procurement software vendors and any company seeking to understand the buyer landscape for enterprise software deals. ## What's Included Verified contacts with VP Procurement, Director of Procurement, Head of Purchasing, Chief Procurement Officer, or VP Purchasing titles. Direct email and phone with company firmographics. ## Use Cases - **Procurement and spend management platforms** (Coupa, Zip, Zip HQ) reaching their primary executive buyer - **Contract lifecycle management vendors** selling to procurement leaders who own vendor agreements - **Supplier risk and management platforms** targeting the function responsible for third-party risk ================================================================================ # Procurement Leaders at Manufacturing Companies > 23,600+ Director and VP-level procurement leaders at manufacturing companies — verified contacts, supply chain scope, and supplier network context **Source:** https://gtm.ai/marketplace/procurement-leaders-manufacturing --- ## Overview This audience covers Director and VP-level procurement, sourcing, and supply chain leaders at manufacturing companies across automotive, aerospace, industrial equipment, electronics, chemicals, food and beverage, and other manufacturing sub-sectors. Contacts are enriched with annual spend estimates, known procurement and ERP systems, and manufacturing sub-sector classification — giving vendors the context to segment by supply chain complexity and technology landscape. Global coverage reflects the international nature of manufacturing supply chains, with strong density in North America, Europe, and Asia. ## What's Included - **Identity**: Full name, verified email, direct phone, and LinkedIn profile URL - **Role Context**: Current title (VP Procurement, Director of Strategic Sourcing, Chief Supply Chain Officer) and scope (direct vs. indirect, global vs. regional) - **Company Context**: Company name and manufacturing sub-sector (automotive, aerospace, industrial, electronics, consumer goods) - **Spend Estimates**: Annual procurement spend estimates where available - **Tech Stack**: Known procurement systems (Ariba, Coupa, Jaggaer) and ERP platform (SAP, Oracle, Infor) ## Use Cases ### Procurement Software and Sourcing Platforms E-procurement, strategic sourcing, contract lifecycle management, and spend analytics platforms target VP and Director-level procurement leaders as primary buyers. ERP and procurement stack enrichment identifies accounts running on legacy systems or lacking modern sourcing infrastructure — key displacement signals. ### Industrial Supplier and MRO Sales MRO distributors, industrial suppliers, and indirect procurement service providers target procurement leaders at manufacturers with large and complex indirect spend programs. Annual spend estimates help prioritize accounts by potential contract value. ### Supply Chain Risk and Resilience Tools Supply chain risk monitoring, supplier financial health, and multi-tier visibility platforms target CPOs and VP-level procurement leaders at manufacturers with concentrated or geographically exposed supply chains. Manufacturing sub-sector filters identify the highest-risk verticals (automotive, semiconductors, aerospace). ### ERP and Operations Consulting Systems integrators and management consultants selling ERP implementations or supply chain transformation programs target VP Procurement and COO-level contacts at manufacturers with large, complex operations where ERP modernization ROI is highest. ================================================================================ # Product Managers at Enterprise Companies > 393,000+ Product Managers at companies with $100M+ revenue — verified contacts, product focus, and company context **Source:** https://gtm.ai/marketplace/product-managers-enterprise --- ## Overview This audience covers 393,000+ Product Managers at companies with $100M or more in revenue — the segment where product management is a defined organizational function with tooling budgets, structured processes, and multiple PM layers. Seniority classification distinguishes Associate PMs and PMs from Senior PMs, Group PMs, and Directors of Product — a critical dimension since tooling and training budgets are controlled at different levels depending on company structure. Product domain and industry data enable vertical-specific messaging for PMs working in fintech, healthcare, enterprise software, and consumer products. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and tenure at current company - **Company Data**: Company name, revenue tier, industry, and employee count - **Product Domain**: Primary product area (platform, growth, core product, data, API, infrastructure) - **Team Context**: Engineering team size and design team presence where available ## Use Cases ### Product Management Tooling Sales Target product managers at enterprise companies evaluating roadmapping, user feedback, analytics, and experimentation platforms. Revenue tier filters ensure focus on organizations with the PM team scale to justify multi-seat product management tooling contracts. ### Design and Research Platform Sales Reach PMs at large companies who co-own or influence design system, user research, and prototyping tool decisions alongside their design team peers. Product domain data surfaces PMs working in customer-facing products where UX investment is highest. ### PM Community and Training Programs Build invite lists for product management certification programs, executive PM roundtables, and practitioner communities. Seniority segmentation enables separate tracks for early-career PMs and senior product leaders with different learning and networking needs. ### Technical Recruiting and PM Sourcing Source Product Managers at enterprise companies for open PM roles requiring domain expertise. Product domain and company industry data enable recruiters to identify PMs with the specific vertical and technical background required for specialized positions. ================================================================================ # Product Marketing Leaders > VP, Director, and Head of Product Marketing professionals — the messaging strategists who define positioning, competitive differentiation, and go-to-market narrative. **Source:** https://gtm.ai/marketplace/product-marketing-leaders --- ## Overview Product Marketing leaders own the narrative — positioning, competitive intelligence, sales messaging, and launch strategy. They're buyers of competitive research tools, content platforms, and enablement software, and they're influential decision-makers in the broader marketing tech evaluation. With 32,000+ verified product marketing leaders globally, this audience is essential for vendors serving the PMM function. ## What's Included Verified contacts with VP Product Marketing, Director of Product Marketing, or Head of Product Marketing titles. Direct email and phone with company firmographics. ## Use Cases - **Competitive intelligence platforms** (Crayon, Klue, Kompyte) reaching their primary buyer - **Sales content and battle card tools** targeting product marketers who create seller-facing assets - **Category design and positioning consultants** reaching senior product marketers responsible for narrative ================================================================================ # Professional Services Firms in New York > 249,000+ consulting, legal, accounting, and professional services firms in New York state with employee count and decision-maker contacts **Source:** https://gtm.ai/marketplace/professional-services-firms-new-york --- ## Overview New York is the largest concentration of professional services firms in the US — spanning financial advisory, management consulting, law, accounting, staffing, and specialized B2B services. This audience covers 249,000+ professional services firms in New York state with category classification, specialization data, revenue estimates, and verified partner and executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and New York state headquarters - **Services Category**: Management consulting, legal, accounting/audit, financial advisory, staffing, or IT services - **Headcount**: Employee count with 1-year growth rate - **Revenue Estimates**: Model-derived revenue ranges based on headcount and specialization - **Client Focus**: Industry or functional specialization where available (financial services, healthcare, technology, real estate) - **Contacts**: Verified email and phone for partners, managing directors, practice leads, and C-suite ## Use Cases ### Professional Services Software Sales Practice management, time and billing, document management, and client portal vendors should use services category and firm size to segment their market. A 20-person boutique law firm and a 500-person accounting firm have different software requirements and procurement cycles — category and headcount enable precise targeting. ### Business Services and Outsourcing Outsourcing providers in payroll, IT, marketing, and finance should target professional services firms that have grown to the point where internal functions are becoming a distraction from client delivery. Revenue estimates and headcount growth identify which firms are at this inflection point. ### Financial and Legal Tech Sales Fintech, RegTech, and legal technology vendors selling to professionals rather than enterprises should use services category and client focus to identify firms with the industry exposure and client concentration that makes their product most relevant. A financial services-focused law firm is a very different buyer than a general practice. ### Recruiting and Executive Search Executive search firms, legal recruiters, and specialized professional services recruiters can use this audience to identify firms in active growth mode. Headcount growth rate identifies which firms are expanding practices — and therefore most actively recruiting partners, associates, and senior staff. ================================================================================ # Public SaaS Companies in the US > 609 US-listed public SaaS companies with financials, executive contacts, tech stack, and investor relations data **Source:** https://gtm.ai/marketplace/public-saas-companies-us --- ## Overview This audience covers all 609 US-listed public SaaS companies, combining SEC-reported financials with ZoomInfo's contact graph and technology intelligence. Each record includes verified executive contacts, ARR estimates, and tech stack coverage across GTM, engineering, and finance functions. Institutional ownership data and analyst coverage add investor relations context not available in standard B2B databases. ## What's Included - **Company Financials**: Revenue, ARR estimates, and growth rates sourced from public filings and earnings calls - **Executive Contacts**: C-suite and VP-level contacts with verified email and direct phone - **Tech Stack**: Business application coverage across CRM, ERP, marketing, and engineering tooling - **Investor Relations**: Institutional ownership percentages and sell-side analyst coverage - **Company Profile**: Ticker, exchange, employee count, HQ location, and founding year ## Use Cases ### Enterprise SaaS Competitive Intelligence Map the full landscape of US public SaaS companies by ARR range, growth rate, and tech stack. Use this audience to benchmark competitors, identify whitespace, and track executive movement across publicly traded peers. ### Public Company Executive Targeting Reach C-suite and VP-level decision-makers at all 609 US-listed SaaS companies with verified contact data. Combine with financial signals — revenue growth, employee headcount changes — to prioritize outreach by company momentum. ### Investor Relations and Capital Markets Target finance and IR executives at public SaaS companies for capital markets services, financial software, and investor communication platforms. Institutional ownership data helps segment by ownership concentration. ### Channel and Partnership Development Identify partnership and business development leads at public SaaS companies aligned to your product category. Tech stack data surfaces existing integrations and partner ecosystem signals. ================================================================================ # Recently Acquired Companies > 1,700+ companies that have recently become subsidiaries of a parent organization — businesses navigating integration, system consolidation, and new ownership mandates. **Source:** https://gtm.ai/marketplace/recently-acquired-companies --- ## Overview Acquisitions trigger a predictable wave of technology and process change: system consolidations, data migrations, HR integrations, and rebranding. Companies that have recently become subsidiaries of a larger organization face intense pressure to align with parent company standards while maintaining operational continuity. This audience of 1,700+ recently acquired companies represents a consistent demand signal for integration and consolidation vendors. ## What's Included Company records confirmed to have a parent-subsidiary relationship established recently, including parent company identification and firmographic data for the acquired entity. ## Use Cases - **ERP and systems integration vendors** targeting companies undergoing post-acquisition tech consolidation - **HR and payroll integration specialists** supporting workforce merges and benefits alignment - **Rebranding and identity vendors** reaching companies navigating ownership transitions ================================================================================ # Recently Funded Startups (Last 90 Days) > 2,900+ companies that closed a funding round in the last 90 days — fresh capital, new mandates, and active vendor evaluation cycles **Source:** https://gtm.ai/marketplace/recently-funded-startups-90-days --- ## Overview A funding close is one of the most reliable purchase intent signals in B2B sales. Companies that have just raised are mandated to deploy capital, actively evaluating new vendors, and expanding headcount — often simultaneously. This audience captures 2,900+ companies that closed a funding round within the last 90 days, refreshed daily from Crunchbase, SEC filings, and press announcement parsing, with verified executive contacts and firmographic context. ## What's Included - **Company Identity**: Verified name, primary domain, and headquarters location - **Funding Event**: Round type (seed, Series A–D, growth equity), round size, and close date - **Investor Data**: Lead investor name, fund, and co-investors where available - **Growth Signals**: Current employee count with 6-month growth rate - **Product Category**: Industry classification and product type (SaaS, marketplace, hardware, services) - **Executive Contacts**: Verified email and phone for founders, C-suite, and VP-level contacts ## Use Cases ### Post-Funding Outreach The 30–60 day window after a funding announcement is the highest-conversion period for new vendor outreach. Use round size and stage to segment messaging — a $5M seed raise has different priorities than a $50M Series C scaling into enterprise. ### Sales Tool and Infrastructure Sales Newly funded companies are building or rebuilding their GTM stack. CRM, sales engagement, data enrichment, and revenue intelligence vendors should prioritize this audience as a high-intent, low-resistance entry point into new accounts. ### Financial Services and Banking Banks, accounting firms, and fintech platforms targeting startups should build triggers around funding events. A new Series B company needs a business bank account, corporate card, financial reporting, and audit-ready accounting infrastructure — often within weeks of closing. ### Recruiting and Talent Services Funding is almost always followed by hiring. Use this audience to target recently funded companies with staffing, recruiting technology, executive search, and employer branding services before they've committed to an existing vendor. ================================================================================ # Recently Promoted VPs (Last 90 Days) > 39,500+ contacts who were promoted to VP-level in the last 90 days — internal promotions with fresh mandates and expanded authority **Source:** https://gtm.ai/marketplace/recently-promoted-vps-90-days --- ## Overview Internal promotions to VP-level represent a distinct buying signal from external hires — these are individuals who know the company deeply, have established relationships, and now hold expanded budget authority for the first time. Recently promoted VPs are motivated to demonstrate impact in their new role, making them active evaluators of tools and services that can accelerate their team's performance. This audience is separate from the new VP hires audience and captures the internal promotion cohort specifically. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **New Title and Department**: Full VP title and functional department (Sales, Marketing, Engineering, Finance, Operations, etc.) - **Promotion Date**: Date the promotion was detected from LinkedIn profile updates and company announcements - **Prior Title Context**: Previous title at the same company to confirm the internal promotion signal - **Company Profile**: Company name, industry, revenue range, and employee count - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Post-Promotion Relationship Building Newly promoted VPs are at a career high point — they're motivated, energized, and open to relationships that can help them succeed in their expanded role. Outreach that acknowledges their promotion and connects it to a specific challenge they'll face in their new position is well-received. This is a lower-friction entry point than cold outreach because the individual has a clear reason to be receptive. ### Tool and Platform Sales to Newly Empowered VPs Internally promoted VPs often inherit their predecessor's tool stack and immediately identify gaps. A newly promoted VP of Marketing who spent three years as Director knows exactly what the team needs that it doesn't have. Use functional title to align your product to the specific gaps most common in that VP's domain — and lead with the team productivity and ROI story, not features. ### Executive Coaching and Leadership Development Executive coaches, leadership development programs, and management training providers target newly promoted VPs as their highest-intent audience. Internal promotions often come without the formal leadership development support that external executive hires receive — creating demand for coaching, peer programs, and management training from individuals investing in their own transition to VP-level leadership. ### ABM Campaigns Targeting Newly Promoted Decision-Makers At target accounts, VP promotions signal a power shift — new budget authority, new priorities, and new vendor evaluation windows. ABM programs that trigger on VP promotions at named accounts can re-activate stalled opportunities or open new conversations that were previously blocked by the prior decision-maker. Filter by function to identify promotions in your primary buyer role. ================================================================================ # Recommend Contacts > Get AI-ranked contact recommendations at a target company, personalized to your ZoomInfo interaction history and CRM win patterns — with engagement priority guidance. **Source:** https://gtm.ai/marketplace/zoominfo-recommend-contacts --- ## Overview The Recommend Contacts skill surfaces AI-ranked contact recommendations at a specific company, personalized to you based on your ZoomInfo platform activity and — when integrated with your CRM — your historical win patterns. It answers the question revenue reps face constantly: "Who at this account should I actually talk to?" Unlike a generic contact search, recommendations are ranked by both relevance to your past engagement patterns and contact reachability (accuracy score). The skill supports three distinct use cases that pull from different signals: cold prospecting based on your ZoomInfo browsing history, deal acceleration based on contacts in closed-won new business, and renewal & growth based on contacts in expansion wins. Results are enriched with full contact details — email, direct dial, title, department — and the skill explains why each person was recommended based on the reference contacts that drove the suggestion. A final engagement priority section identifies who to contact first, with reasoning about entry point vs. decision-maker sequencing. ## What It Does - **Three recommendation modes**: Prospecting (based on ZoomInfo platform activity), Deal Acceleration (based on CRM closed-won new business), and Renewal & Growth (based on CRM expansion wins) - **Personalized ranking**: Recommendations reflect your team's specific engagement history, not generic popularity - **Full contact enrichment**: Each recommended contact is enriched with email, direct phone, management level, and accuracy score - **Why-recommended explanations**: The reference contact driving each recommendation is surfaced so you understand the signal behind the suggestion - **Engagement priority guidance**: Top 5 contacts to engage first, with reasoning about sequence — entry points vs. decision-makers ## Use Cases ### Net-New Account Prospecting When entering a new account cold, use the Prospecting mode to get a ranked list of contacts based on who you and your team have engaged with at similar accounts on ZoomInfo. The recommendations prioritize personas that have historically been high-signal for your team — not just the most senior titles. ### Deal Acceleration During an active opportunity, use Deal Acceleration mode to find additional stakeholders at the account who match the contact patterns from your past closed-won deals. Expand your coverage of the buying committee with contacts most likely to be relevant to your specific selling motion. ### Renewal and Expansion Planning Ahead of a renewal cycle or expansion conversation, use Renewal & Growth mode to surface contacts whose profiles align with the champions and influencers from your successful renewal playbook. Identify who to loop in before the contract conversation starts. ================================================================================ # Repeat Founders > 273,000+ individuals who have founded more than one company, identified through verified employment history showing multiple founder or co-founder roles. **Source:** https://gtm.ai/marketplace/repeat-founders --- ## Overview Serial founders represent one of the highest-signal audiences in B2B — they've been through company building before, understand the tools and vendors they need, and tend to make decisions fast. This audience of 273,000+ verified repeat founders is built from employment history showing two or more founder or co-founder roles across different companies. ## What's Included Records include verified resume history confirming multiple founding roles, current company details, and direct contact information. Founders are distributed across industries, company sizes, and geographies, with the heaviest concentration in software, fintech, and business services. ## Use Cases - **VCs and angel investors** building deal flow pipelines with high-conversion founder audiences - **B2B tool vendors** reaching decision-makers who've bought and built multiple stacks - **Accelerators and studios** recruiting experienced operators for new ventures ================================================================================ # Retail Brands in the Southeast US > 1,500,000+ retail companies across Florida, Georgia, North Carolina, South Carolina, Tennessee, and Alabama **Source:** https://gtm.ai/marketplace/retail-brands-southeast --- ## Overview The Southeast US is one of the fastest-growing retail markets in the country, driven by population growth in Florida, Georgia, and the Carolinas. This audience covers 1.5M+ retail companies — from single-location specialty shops to regional chains with multi-state footprints. Records span grocery, fashion, home goods, automotive retail, and specialty categories, with verified contacts across operations, finance, and executive leadership. ## What's Included - **Company Identity**: Name, website, HQ state, and store location data where available - **Retail Category**: Specialty, grocery, fashion, home and garden, automotive, or mixed-use classification - **Scale Signals**: Employee count, estimated store count, and revenue range estimates - **Regional Footprint**: Primary state presence and multi-state expansion signals - **Revenue Estimates**: Modeled annual revenue ranges based on store count, category, and employee data - **Decision-Maker Contacts**: Operations, finance, marketing, and C-suite contacts with verified email and phone ## Use Cases ### Southeast Retail Technology Sales POS systems, inventory management, workforce scheduling, and e-commerce platforms all have strong demand signals in Southeast retail. Use employee count and store count filters to segment by deployment complexity — single-location retailers need different solutions than regional chains running 50+ stores. ### Supply Chain and Logistics Services Southeast retail companies are increasingly building direct-to-consumer and omnichannel fulfillment capabilities. Logistics providers, 3PLs, and supply chain technology vendors can use this audience to target operations and supply chain contacts at retailers actively expanding their distribution footprint. ### Marketing and Loyalty Platform Sales Regional retail brands in the Southeast are competing with national chains for customer loyalty. Email marketing, SMS, loyalty program, and customer data platform vendors can target marketing directors and CMOs at mid-size retail companies with the budget and motivation to invest in retention technology. ### Regional Retail Consulting and Advisory Strategy, operations, and technology consulting firms serving retail can use this audience to map the Southeast market, identify growing regional chains, and target PE-backed retail portfolios. Filter by revenue tier and employee growth to prioritize the most active buyers. ================================================================================ # Retail Executives in the US > 1,151,000+ VP and C-suite executives at US retail companies — verified contacts spanning specialty, grocery, e-commerce, and big-box retail **Source:** https://gtm.ai/marketplace/retail-executives-us --- ## Overview This audience covers VP and C-suite executives across the full spectrum of US retail — specialty retail, grocery, apparel, home goods, e-commerce, and big-box chains. With over 1.1 million contacts, it is the broadest retail executive layer in ZoomInfo's database. Contacts are enriched with retail category classification, store count estimates, revenue range, and known e-commerce and omnichannel tech stack — enabling both broad market coverage plays and highly segmented campaigns by retail vertical. ## What's Included - **Identity**: Full name, verified email, direct phone, and LinkedIn profile URL - **Role Context**: Current title, management level, and functional area (merchandising, technology, operations, finance, marketing) - **Company Context**: Retail category (specialty, grocery, apparel, big-box, e-commerce), store count, and revenue range - **Tech Stack**: Known e-commerce platform (Shopify, Salesforce Commerce Cloud, Magento), POS systems, and omnichannel infrastructure - **Org Size**: Employee count and physical footprint ## Use Cases ### Retail Technology and POS Sales Point-of-sale, inventory management, and store operations platforms can segment this audience by store count and retail category to identify accounts with the right scale and vertical fit. Store count filters separate single-location independents from regional chains and national operators. ### Supply Chain and Inventory Management Retail supply chain software, demand forecasting, and fulfillment optimization tools target VP-level supply chain and operations leaders. Revenue range and store count help prioritize by account complexity and deal potential. ### Marketing and Loyalty Platform Sales Customer data platforms, loyalty program software, and retail media networks target CMOs, VPs of Marketing, and digital commerce leaders. Tech stack enrichment identifies accounts already using adjacent tools — or those running on legacy systems that signal displacement opportunity. ### Retail Consulting and Advisory Strategy, operations, and digital transformation consulting firms targeting retail decision-makers can use retail category and revenue range to qualify accounts by transformation readiness and budget capacity. ================================================================================ # Revenue Operations Leaders > 17,300+ RevOps and Revenue Operations professionals at the VP, Director, and Head of level — the architects of the modern GTM tech stack. **Source:** https://gtm.ai/marketplace/revenue-operations-leaders --- ## Overview Revenue Operations leaders own the GTM tech stack, CRM configuration, sales process design, and go-to-market data infrastructure. They're among the most influential and frequent buyers in B2B SaaS — responsible for tool evaluation, vendor relationships, and the systems that sales, marketing, and customer success teams run on. With 17,300+ verified RevOps leaders, this audience is essential for any vendor selling into the revenue stack. ## What's Included Verified contacts with Revenue Operations, RevOps, or related titles at the VP, Director, or Head of level. Direct email, phone, and company firmographics enable both targeted outreach and account-based campaigns. ## Use Cases - **CRM, sales engagement, and marketing automation vendors** reaching the administrators and owners of their tools - **Data quality and enrichment vendors** targeting the people responsible for CRM hygiene - **RevOps consulting firms** prospecting companies with dedicated revenue operations leaders ready for optimization support ================================================================================ # Revenue Operations Leaders in the US > 9,300+ Revenue Operations leaders in the United States — verified contacts, company revenue stack, and operational scope **Source:** https://gtm.ai/marketplace/revops-leaders-us --- ## Overview This audience covers 9,300+ Revenue Operations leaders in the US — a relatively concentrated and high-value population that controls the data infrastructure, tooling stack, and operational processes underlying sales, marketing, and customer success functions. RevOps leaders are the primary buyers for CRM, attribution, revenue intelligence, and go-to-market data products at B2B companies. Team scope data distinguishes unified RevOps organizations from siloed sales ops or marketing ops functions — a meaningful difference in buying authority and budget control. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, RevOps scope (unified, sales ops, marketing ops, CS ops), and tenure - **Company Data**: Company name, ARR range estimate, and employee count - **RevOps Tech Stack**: Known tools across CRM, BI, attribution, forecasting, and data enrichment - **Team Scope**: Functional coverage and team headcount estimate ## Use Cases ### RevOps Platform and Data Tool Sales Reach RevOps leaders who are the primary evaluators and buyers for go-to-market data, enrichment, and operational tooling. Tech stack data surfaces incumbents and gaps — enabling outreach that references their specific environment rather than generic platform pitches. ### CRM and Attribution Solutions Target RevOps leaders managing CRM health and marketing attribution across complex, multi-touch revenue motions. ARR range filters help identify organizations where CRM data quality and attribution accuracy have measurable pipeline and revenue impact. ### Revenue Intelligence and Forecasting Tools Identify RevOps leaders at B2B companies evaluating revenue intelligence, pipeline analytics, and AI-assisted forecasting platforms. Team scope data surfaces unified RevOps organizations — the accounts most likely to consolidate forecasting and analytics into a single platform. ### RevOps Consulting and Advisory Source leads for RevOps consulting engagements, tech stack audits, and process optimization advisory. Tenure data surfaces recently hired RevOps leaders in their first year — the window where new leaders are most receptive to external expertise and technology change. ================================================================================ # SaaS Companies in the Pacific Northwest > 48,400+ SaaS and software companies in Washington and Oregon — home to Amazon, Microsoft, and a dense ecosystem of cloud-native companies **Source:** https://gtm.ai/marketplace/saas-companies-pacific-northwest --- ## Overview The Pacific Northwest — centered on the Seattle-Bellevue metro but extending to Portland — is one of the world's most significant software and cloud computing clusters. Amazon Web Services, Microsoft Azure, and a dense ecosystem of cloud-native SaaS companies have made the region a hub for developer tools, enterprise software, e-commerce technology, and cloud infrastructure. This audience covers 48,400+ SaaS and software companies across Washington and Oregon, enriched with tech stack, cloud platform affinity, funding data, and revenue estimates. ## What's Included - **Company Identity**: Company name, website, and metro location (Seattle, Bellevue, Portland, or surrounding area) - **Size and Growth**: Employee count and year-over-year growth rate - **Funding**: Stage and total capital raised - **Tech Stack**: Primary cloud platform (AWS, Azure, GCP), development stack, and known SaaS tools in use - **Revenue**: ARR or revenue range estimates - **Contacts**: Key decision-maker contacts at VP and C-suite level ## Use Cases ### Pacific Northwest Tech Ecosystem Sales B2B software vendors with Pacific Northwest territory coverage can build comprehensive account lists across the Seattle-Portland corridor — segmented by company size, funding stage, and tech stack to prioritize accounts by deal potential and product fit. ### Cloud and Developer Tool Sales The Pacific Northwest's developer-heavy ecosystem creates concentrated demand for developer tools, cloud cost management, DevOps platforms, and API infrastructure. Cloud platform filters identify companies with deep AWS or Azure commitments — enabling cloud-native positioning and ecosystem-aligned outreach. ### Pacific Northwest Investor and VC Outreach Venture capital firms and growth equity investors active in the Pacific Northwest can use funding stage and revenue growth data to identify companies approaching the next capital raise threshold — particularly useful in a region where many companies reach significant scale before formal venture funding. ### Startup and Scale-Up Community Targeting Accelerators, tech communities, and ecosystem organizations in the Seattle and Portland startup scenes can use funding stage, employee count, and founding date to build targeted invitation and outreach lists for programs, events, and community initiatives tailored to specific company growth stages. ================================================================================ # Sales Development Leaders > VP, Director, and Head of Sales Development (SDR/BDR) leaders — the managers responsible for outbound pipeline generation and the primary buyers of prospecting and outreach technology. **Source:** https://gtm.ai/marketplace/sales-development-leaders --- ## Overview Sales Development leaders — Heads of SDR, VP of Business Development, Director of Sales Development — are the architects of outbound pipeline generation. They're responsible for sequencing strategy, prospecting data quality, outreach tooling, and SDR coaching. They're among the most active buyers of sales intelligence, engagement, and automation technology. With 18,000+ verified contacts, this audience targets the leaders running the top-of-funnel pipeline machine. ## What's Included Verified contacts with Head of SDR, VP of Sales Development, Director of Sales Development, Head of Business Development (SDR function), or Head of BDR titles. Direct email and phone with company firmographics. ## Use Cases - **Prospecting data and intent vendors** reaching leaders responsible for SDR data quality and targeting - **Sales engagement platforms** targeting the managers who select and configure outbound tools - **SDR coaching and training firms** selling to leaders responsible for rep development and performance ================================================================================ # Sales Directors at Enterprise SaaS Companies > 48,300+ Sales Directors at enterprise SaaS companies with $100M+ revenue — verified contacts, team size, and sales tech stack **Source:** https://gtm.ai/marketplace/sales-directors-enterprise-saas --- ## Overview This audience covers 48,300+ Sales Directors at enterprise SaaS companies with $100M or more in ARR — the segment where sales technology investment, quota structure, and process standardization are mature enough to create consistent buying patterns. Known sales tech stack across CRM, sales engagement platforms, and conversation intelligence tools enables sellers to identify incumbent relationships, surface displacement opportunities, and sequence competitive messaging before outreach. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, segment ownership (enterprise, commercial, mid-market), and tenure - **Company Data**: Company name, ARR range estimate, and employee count - **Sales Team**: Estimated AE headcount, SDR count, and sales team structure - **Sales Tech Stack**: Known tools across CRM, SEP, CI, forecasting, and enablement categories ## Use Cases ### Sales Technology and Engagement Platform Sales Target Sales Directors at enterprise SaaS companies who control sales engagement, CRM, and conversation intelligence budgets. Tech stack data surfaces accounts running legacy tools or missing key platform categories — making outreach specific and avoiding pitches to satisfied customers with locked-in contracts. ### Sales Training and Enablement Reach sales leaders at enterprise SaaS companies with established sales teams actively investing in rep productivity and skill development. Sales team size estimates help prioritize accounts where training ROI scales across a large enough headcount to justify program investment. ### Revenue Operations Tool Sales Identify Sales Directors at enterprise SaaS companies evaluating forecasting, pipeline analytics, and territory management platforms. ARR range and team size data segment by companies at the scale where RevOps tooling provides measurable leverage. ### Recruiting and Sales Leadership Sourcing Source Sales Directors who are potential candidates for VP of Sales or CRO roles, or who control sales hiring at enterprise SaaS companies. ARR range and team size indicate the scale and complexity of each leader's experience. ================================================================================ # Sales Enablement Leaders > 3,100+ VP, Director, and Head of Sales Enablement professionals — the buyers responsible for sales content, training, readiness, and the enablement tech stack. **Source:** https://gtm.ai/marketplace/sales-enablement-leaders --- ## Overview Sales Enablement has emerged as a dedicated function at companies with mature sales organizations — and the leaders running it are primary buyers for content management, LMS, coaching, and readiness platforms. With 3,100+ verified enablement leaders, this audience targets the exact function that owns the evaluation and purchase of sales enablement technology. ## What's Included Verified contacts with Sales Enablement VP, Director, or Head of Sales Enablement titles. Direct email and phone with company firmographics for filtering by company size and industry. ## Use Cases - **Sales enablement platforms** (Highspot, Seismic, Showpad) targeting the leaders who evaluate and deploy them - **Sales content management and digital rooms** reaching enablement professionals managing seller content - **Sales coaching and readiness tools** selling to enablement leaders responsible for rep development ================================================================================ # Sales Engineers at Tech Companies > 80,600+ Sales Engineers at software and technology companies — verified contacts, product domain, and company context **Source:** https://gtm.ai/marketplace/sales-engineers-tech --- ## Overview This audience covers 80,600+ Sales Engineers at software and technology companies — the technical pre-sales practitioners who own proof-of-concept delivery, product demonstrations, and technical evaluation support across the enterprise sales cycle. Sales Engineers are the primary buyers and influencers for demo automation platforms, sandbox environments, and SE enablement tooling. Technical domain and specialization data enable targeting by product category — infrastructure SEs, data platform SEs, and security SEs operate in distinct technical environments with different tooling requirements. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title (Sales Engineer, Solutions Engineer, Solutions Architect), tenure, and seniority - **Company Data**: Company name, product category, and revenue range - **Technical Domain**: Primary technical specialization (cloud, security, data, infrastructure, application) - **Sales Structure**: Territory scope (enterprise, commercial, SMB) where available ## Use Cases ### SE Tooling and Demo Platform Sales Target Sales Engineers who are the direct users and frequent champions of demo automation, interactive product tour, and sandbox provisioning platforms. Technical domain data enables outreach tailored to the specific demo challenges faced by cloud, security, or data platform SEs. ### Technical Sales Recruiting and Staffing Source experienced Sales Engineers at tech companies for SE, Solutions Architect, and Technical Account Manager roles. Technical domain and company product category data enable recruiters to match candidates to open roles requiring vertical or platform expertise. ### Sales Engineering Training and Certification Reach Sales Engineers across the full seniority spectrum for technical sales training, discovery skills programs, and SE career development certifications. Company size and seniority data help training providers identify organizations with enough SE headcount to justify cohort-based programs. ### Pre-Sales Consulting and Advisory Identify SE leaders and individual contributors at tech companies for advisory services around SE team structure, process design, and tool stack optimization. Tenure and seniority data surface senior SEs and SE managers most likely to engage external expertise. ================================================================================ # Salesforce + Gong Stack > 480 companies running both Salesforce CRM and Gong — organizations with elite revenue intelligence infrastructure and data-driven sales cultures. **Source:** https://gtm.ai/marketplace/salesforce-gong-stack --- ## Overview Companies running both Salesforce and Gong represent the upper tier of revenue operations maturity — they've invested in both the system of record (Salesforce) and the conversation intelligence layer (Gong), and they're likely buyers of forecasting, enablement, and pipeline analytics tools that bridge the two. With 480 confirmed dual-stack installations, this is a small but extremely high-value audience. ## What's Included Confirmed installations of both Salesforce and Gong alongside company firmographics. These are typically mid-market and enterprise B2B companies with significant ACV, quota-carrying sales teams, and mature revenue operations. ## Use Cases - **Sales forecasting platforms** selling to organizations with structured CRM data and call intelligence - **Sales enablement vendors** targeting companies that actively coach on call recordings - **Revenue analytics tools** building on the Salesforce + Gong data combination ================================================================================ # Salesforce + Marketo + Gong Triple Stack > 293 companies running Salesforce, Marketo, and Gong simultaneously — the most advanced B2B revenue stack signal available, representing elite GTM infrastructure maturity. **Source:** https://gtm.ai/marketplace/salesforce-marketo-gong-stack --- ## Overview Only 293 companies in ZoomInfo's database run Salesforce, Marketo, and Gong simultaneously — making this the most precise, highest-maturity signal in the B2B GTM tech stack landscape. These organizations have invested in a complete revenue infrastructure spanning CRM, marketing automation, and conversation intelligence. They're sophisticated buyers evaluating equally sophisticated tools. ## What's Included Confirmed installations of all three platforms alongside company firmographics. These are typically well-funded mid-market to enterprise B2B companies with dedicated sales, marketing, and revenue operations functions. ## Use Cases - **Enterprise revenue intelligence platforms** targeting the most instrumented GTM organizations - **ABM and account-based platforms** selling to companies with mature demand generation and deal management - **GTM consulting firms** prospecting organizations investing in revenue stack optimization ================================================================================ # Salesforce + Marketo Stack > 10,800+ companies running both Salesforce CRM and Marketo — the classic enterprise B2B demand generation stack requiring specialized integration and data management. **Source:** https://gtm.ai/marketplace/salesforce-marketo-stack --- ## Overview The Salesforce + Marketo combination is the gold standard enterprise B2B demand generation stack — and one of the most complex to manage. Companies running both platforms are active buyers of integration middleware, data quality tools, attribution software, and RevOps consulting. This audience of 10,800+ companies defines the TAM for Marketo-Salesforce ecosystem vendors. ## What's Included Confirmed installations of both Salesforce and Marketo alongside company firmographics. The audience skews toward mid-market and enterprise B2B companies with defined marketing and sales operations functions. ## Use Cases - **Marketing operations consultants** and Marketo implementation partners - **Data quality and enrichment vendors** solving the lead-to-account matching problem - **ABM platforms** selling to companies with mature Salesforce-Marketo infrastructure ================================================================================ # Salesforce + ServiceNow Stack > 16,300+ companies running both Salesforce and ServiceNow — large enterprises managing distinct sales CRM and IT service management platforms that need workflow integration. **Source:** https://gtm.ai/marketplace/salesforce-servicenow-stack --- ## Overview Salesforce + ServiceNow is a hallmark enterprise technology stack — Salesforce drives revenue operations while ServiceNow manages IT and internal workflows. These 16,300+ companies are large enough to need both platforms, and they're active buyers of tools that bridge customer-facing and internal operational workflows. This is a premium enterprise audience. ## What's Included Confirmed dual installations of Salesforce and ServiceNow with company firmographics. The audience skews toward large enterprises with mature IT and sales operations functions — companies investing heavily in enterprise software infrastructure. ## Use Cases - **Enterprise integration platforms** (MuleSoft, Boomi, Workato) targeting complex multi-platform environments - **Professional services automation tools** bridging sales delivery and IT service - **AI workflow vendors** selling to companies with mature enterprise automation stacks ================================================================================ # Salesforce + Snowflake Stack > 12,600+ companies running both Salesforce CRM and Snowflake — organizations that have unified their go-to-market data with their cloud data infrastructure. **Source:** https://gtm.ai/marketplace/salesforce-snowflake-stack --- ## Overview Companies running both Salesforce and Snowflake represent the intersection of go-to-market operations and modern data infrastructure — they've invested in both the CRM layer and the data warehouse layer, and they're actively looking to connect them. This audience of 12,600+ companies is a high-value target for data activation, reverse-ETL, and RevOps analytics vendors. ## What's Included Records include confirmed installations of both Salesforce and Snowflake, paired with company firmographics. These companies tend to have mature data and revenue operations functions investing in the modern GTM stack. ## Use Cases - **Reverse-ETL and data activation vendors** (Census, Hightouch) targeting their core use case - **RevOps analytics platforms** built to unify Salesforce pipeline data with Snowflake - **Customer data platforms** targeting companies that need to operationalize warehouse data ================================================================================ # Salesforce + Workday Stack > 9,300+ companies running both Salesforce and Workday — enterprise organizations with coordinated revenue and workforce management infrastructure. **Source:** https://gtm.ai/marketplace/salesforce-workday-stack --- ## Overview Companies running both Salesforce and Workday are managing revenue operations and workforce planning on best-in-class platforms — but these systems don't talk to each other natively. This 9,300+ company audience represents a clear market for sales compensation, headcount planning, and integration vendors that connect pipeline data to workforce decisions. ## What's Included Confirmed installations of both Salesforce and Workday alongside company firmographics. The audience is concentrated in mid-to-large enterprises with established sales organizations and HR/finance functions. ## Use Cases - **Sales compensation and commission management tools** targeting the Salesforce-Workday integration opportunity - **Revenue-based headcount planning platforms** connecting pipeline forecasts to hiring plans - **Enterprise integration vendors** managing Salesforce-Workday data flows for finance and HR ================================================================================ # Salesforce + Zendesk Stack > 10,400+ companies running both Salesforce CRM and Zendesk — organizations with distinct enterprise sales and support infrastructure that need to share customer context. **Source:** https://gtm.ai/marketplace/salesforce-zendesk-stack --- ## Overview Salesforce + Zendesk users have separated their sales CRM from their support platform — a common enterprise pattern that creates data fragmentation between pre-sale and post-sale teams. These 10,400+ companies are active buyers of customer 360, account health, and integration tools that bridge the two systems to give revenue and support teams a unified view of the customer. ## What's Included Confirmed installations of both Salesforce and Zendesk with company firmographics. The audience spans mid-market and enterprise companies with structured sales and customer support organizations. ## Use Cases - **Customer 360 and revenue intelligence platforms** targeting the Salesforce-Zendesk data gap - **Account health and renewal intelligence tools** for customer success teams - **Integration and data sync vendors** managing bidirectional Salesforce-Zendesk flows ================================================================================ # Salesforce Admins and Architects > Salesforce Administrators, Salesforce Architects, and Salesforce Business Analysts — the practitioners who configure, extend, and manage Salesforce deployments. **Source:** https://gtm.ai/marketplace/salesforce-admins-and-architects --- ## Overview Salesforce Admins and Architects are the hands-on operators of the world's most widely deployed CRM — they configure workflows, manage integrations, govern data quality, and implement new features. They're highly influential in Salesforce ecosystem purchasing: if an admin discovers a tool on AppExchange, they often drive its adoption. With 95,000+ verified Salesforce practitioners, this audience is essential for any Salesforce ecosystem vendor. ## What's Included Verified contacts with Salesforce Administrator, Salesforce Admin, Salesforce Architect, Salesforce Developer, or Salesforce Business Analyst titles. Direct email and phone with company firmographics. ## Use Cases - **Salesforce AppExchange vendors** reaching the admins who evaluate and install apps in production Salesforce orgs - **Salesforce training and certification platforms** targeting practitioners investing in their Salesforce skills - **Data quality and deduplication tools** selling to admins responsible for CRM data health ================================================================================ # Salesforce Users with AI Intent > 95,600+ Salesforce CRM users also showing active AI research intent — the most actionable signal for AI-native CRM tools, Einstein competitors, and GTM AI vendors. **Source:** https://gtm.ai/marketplace/salesforce-users-with-ai-intent --- ## Overview Salesforce users showing active AI research intent are evaluating how to bring AI into their existing CRM workflows — whether through Einstein, third-party AI tools, or purpose-built GTM AI platforms. With 95,600+ qualifying companies, this audience represents the core market for AI-native sales intelligence, forecasting, and conversation tools that plug into Salesforce-based revenue stacks. ## What's Included Confirmed Salesforce installations paired with active AI/ML intent signals and composite scores. Company firmographics enable segmentation by size and industry to match the right AI pitch to the right buyer profile. ## Use Cases - **AI CRM vendors** targeting Salesforce users evaluating intelligent automation and next-best-action features - **GTM AI platforms** reaching revenue teams actively researching AI-powered prospecting and engagement - **Sales forecasting AI** selling to Salesforce organizations evaluating Einstein alternatives ================================================================================ # Scaling Tech Companies (50–500 Employees, VC-Backed) > VC-backed technology companies in the 50–500 employee range — the highest-growth segment of the tech market, actively building out their full operational and technology stack. **Source:** https://gtm.ai/marketplace/scaling-vc-backed-tech-companies --- ## Overview VC-backed tech companies in the 50–500 employee range are the fastest-moving buyers in the market — they've raised enough capital to buy real tools, they're scaling fast enough to need them urgently, and they haven't yet locked into the incumbent enterprise vendors. This is the prime window for a wide range of B2B vendors to establish relationships that scale with the company. ## What's Included Company records combining VC funding confirmation, technology industry classification, and 50–500 employee range. Company firmographics, location, and most recent funding round are included. ## Use Cases - **Mid-market SaaS vendors** targeting the high-growth segment that's outgrowing SMB tools - **Talent acquisition and recruiting platforms** reaching companies in aggressive hiring phases - **Finance, legal, and operational services** for high-growth companies formalizing their back-office ================================================================================ # Security Engineers and Analysts > Security Engineers, Security Analysts, SOC Analysts, and Penetration Testers — the hands-on practitioners managing and operating enterprise security infrastructure. **Source:** https://gtm.ai/marketplace/security-engineers-and-analysts --- ## Overview Security Engineers, Analysts, and SOC professionals are the operational layer of enterprise cybersecurity — they triage alerts, investigate incidents, run penetration tests, and configure security tooling. They're the hands-on evaluators for the tools their CISO will eventually buy, and they're deeply influential in vendor selection. With 210,000+ verified security practitioners, this is the largest and most technically credible security buyer audience available. ## What's Included Verified contacts with Security Engineer, Security Analyst, SOC Analyst, Penetration Tester, or Application Security Engineer titles. Direct email and phone with company firmographics. ## Use Cases - **SIEM, SOAR, and detection vendors** reaching the analysts who operate their platforms daily - **Vulnerability management and pen testing tools** targeting the practitioners running security assessments - **Security training platforms** (SANS, Offensive Security) reaching professionals investing in technical skills ================================================================================ # Security Engineers at Financial Services Companies > 40,900+ compliance officers and security professionals at financial institutions — verified contacts, regulatory environment, and company context **Source:** https://gtm.ai/marketplace/security-engineers-financial-services --- ## Overview This audience targets security engineers, compliance officers, and information security professionals working at banks, insurance companies, investment firms, and other financial institutions. Each contact is enriched with their regulatory environment — SOX, PCI DSS, FINRA, or other applicable frameworks — giving you the context to lead with relevant messaging. Coverage is global, with the strongest density in North America and Western Europe. ## What's Included - **Identity**: Full name, verified email, direct phone, and LinkedIn profile URL - **Role Context**: Current title, compliance or security domain (e.g., AppSec, GRC, fraud), and seniority level - **Firm Context**: Company name, financial sector classification (banking, insurance, asset management, payments), and employee count - **Regulatory Environment**: Primary regulatory frameworks applicable to the institution (SOX, PCI DSS, FINRA, GDPR, Basel III) - **Org Structure**: Team size estimates and reporting chain (e.g., reports to CISO or CRO) ## Use Cases ### Financial Compliance and GRC Platform Sales Target compliance officers and security engineers who own audit readiness, risk management, and policy enforcement at regulated financial institutions. Regulatory framework attributes let you segment by SOX vs. PCI vs. FINRA exposure and tailor your value proposition to their specific compliance burden. ### Security Tooling for Regulated Industries Financial services is among the highest-spend verticals for security tooling — WAF, SIEM, DLP, PAM, and endpoint protection all face accelerated buying cycles driven by regulatory pressure. Use this audience to reach the security practitioners making or influencing those purchasing decisions. ### Regulatory Consulting and Audit Services Audit firms, law firms, and boutique consultancies targeting financial services compliance teams can use this audience to identify decision-makers at institutions facing upcoming regulatory deadlines or recent enforcement actions. ### Financial Services Technology Sales Broader finserv technology plays — core banking, fraud detection, identity verification, data governance — benefit from a contact base that spans both technical practitioners and compliance-adjacent roles, enabling both bottom-up and top-down sales motions. ================================================================================ # Senior Leaders in APAC > 3,000,000+ VP and C-suite executives across Asia-Pacific — verified contacts spanning Australia, Japan, Singapore, India, and Southeast Asia **Source:** https://gtm.ai/marketplace/senior-leaders-apac --- ## Overview This audience covers VP and C-suite executives across the Asia-Pacific region — Australia and New Zealand, Japan, South Korea, Singapore, Hong Kong, India, and Southeast Asia (Indonesia, Thailand, Malaysia, Vietnam, Philippines). At 3 million contacts, it provides the scale for market coverage plays alongside the contact quality needed for executive outreach across one of the world's most diverse and fast-growing business regions. Contacts are enriched with country and city, industry, and revenue range to support country-level targeting and regional account programs. ## What's Included - **Identity**: Full name, verified email, phone where available, and LinkedIn profile URL - **Role Context**: Current title and management level (VP, SVP, C-suite) and functional area - **Geography**: Country and city, with sub-regional groupings (ANZ, Northeast Asia, Southeast Asia, South Asia) - **Company Context**: Company name, industry classification, and company type - **Company Size**: Employee count and revenue range ## Use Cases ### APAC Market Entry and Expansion Companies expanding into APAC markets use this audience to build initial target account lists and executive contact layers by country. Country filters support prioritized market entry — typically starting with ANZ or Singapore before expanding into Japan, India, or broader Southeast Asia. ### Regional Enterprise Software Sales Enterprise software vendors with APAC sales teams can segment by country, industry, and company size to align territory assignments, build regional pipeline, and deploy country-specific campaigns. Verified email and phone are critical in markets where outbound norms and preferred channels vary significantly by country. ### Cross-Border Partnership Development Alliance and channel teams building APAC partner ecosystems can identify VP and C-suite leaders at regional distributors, systems integrators, and technology partners. Industry and revenue filters prioritize partner targets by market reach and capability. ### APAC Executive Event and Community Targeting Executive events, industry forums, and analyst summits targeting APAC leadership require invitation lists built by country, industry, and seniority. This audience supports both in-person events in hub cities (Singapore, Sydney, Tokyo) and virtual programs targeting the full APAC executive population. ================================================================================ # Senior Leaders in EMEA > 4,000,000+ VP and C-suite executives across Europe, the Middle East, and Africa — verified contacts with regional and company context **Source:** https://gtm.ai/marketplace/senior-leaders-emea --- ## Overview This audience covers VP and C-suite executives across the full EMEA region — Western Europe, Central and Eastern Europe, the UK, the Nordics, the Middle East, and Africa. At 4 million contacts, it provides the breadth needed for market sizing and territory planning alongside the contact quality needed for executive outreach. Contacts are enriched with country and city, company industry, and revenue range — enabling both country-level segmentation and cross-border account targeting for vendors expanding into EMEA markets. ## What's Included - **Identity**: Full name, verified email, phone where available, and LinkedIn profile URL - **Role Context**: Current title and management level (VP, SVP, C-suite) and functional area - **Geography**: Country and city, with regional sub-groupings (DACH, Nordics, Benelux, UK/Ireland, Southern Europe, MEA) - **Company Context**: Company name, industry classification, and company type - **Company Size**: Employee count and revenue range ## Use Cases ### EMEA Market Entry and Expansion US and global companies entering EMEA markets use this audience to build initial target account lists and executive contact layers for new territories. Country and city filters support staged rollout — starting with UK and DACH before expanding to Southern Europe and MEA. ### Regional Enterprise Software Sales Enterprise software vendors with EMEA sales teams can segment by country, industry, and company size to align territory assignments, build regional pipeline, and support country-specific campaigns with locally relevant messaging and compliant contact data. ### Cross-Border Partnership Development Vendors, distributors, and alliance teams building channel partnerships across EMEA can identify VP and C-suite decision-makers at regional systems integrators, resellers, and technology partners. Industry and revenue filters help prioritize partner targets by market coverage and capability. ### EMEA Executive Event Targeting Conferences, executive dinners, and analyst summit organizers targeting EMEA executives can use geography and industry filters to build invitation lists by country and vertical — essential for events with regional focus or limited seat counts. ================================================================================ # Series A Companies in the US > 5,800+ US companies at Series A stage with funding details, employee count, product category, and founder contacts **Source:** https://gtm.ai/marketplace/series-a-companies-us --- ## Overview Series A companies in the US are at the inflection point between early traction and scaling — they've validated their product, closed institutional capital, and are actively building the teams and infrastructure needed to grow. These 5,800+ companies are making first-time purchases across almost every software category, from CRM to HR to financial systems, often for the first time at a professional scale. The audience is built from ZoomInfo's identity graph and cross-referenced against Crunchbase and SEC filings, with verified founder and executive contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters location - **Funding Event**: Series A round size, close date, and lead investor - **Headcount**: Current employee count with 6-month growth rate - **Product Category**: Industry and software category classification - **Investor Context**: Lead VC name and fund for prioritization against your own investor network - **Founder Contacts**: Verified email and phone for founders, CEO, CTO, and early VP hires ## Use Cases ### Early-Stage SaaS Sales Series A companies are first-time buyers for most enterprise software categories. Products with SMB-to-mid-market pricing and quick time-to-value outperform in this segment — use product category and investor context to prioritize companies with the right growth trajectory. ### VC Portfolio Monitoring Investors and operators tracking the Series A market can use this audience to monitor portfolio companies, identify market trends by vertical and geography, and surface competitive signals across the startup ecosystem. ### Startup-Focused Service Sales Legal, accounting, insurance, HR, and financial services firms that specialize in the startup market should build outreach programs anchored to the Series A event. These companies are formalizing operations and need professional services vendors immediately. ### Recruiting and Talent Acquisition Series A companies are building core teams aggressively. Recruiting firms, staffing platforms, and executive search practices should treat each Series A close as a trigger for outreach — headcount will grow 50–200% in the 18 months following a raise. ================================================================================ # Series B Companies in the US > 3,300+ US companies at Series B stage — post-product-market-fit, scaling teams, and actively building out their GTM stack **Source:** https://gtm.ai/marketplace/series-b-companies-us --- ## Overview Series B companies have achieved product-market fit and are scaling into repeatable revenue. They have dedicated sales and marketing teams, a defined ICP, and real software budgets — but they're still evaluating and replacing tools as they move from startup infrastructure to enterprise-grade systems. This audience captures 3,300+ US Series B companies with verified funding data, tech stack detection across GTM functions, and C-suite and VP-level contacts. ## What's Included - **Company Identity**: Verified name, primary domain, and US headquarters location - **Funding Event**: Series B round size, close date, and lead investor - **Board Composition**: Known board members and investor representatives where available - **Headcount**: Current employee count with department-level breakdown and 1-year growth rate - **Tech Stack**: Detected tools across CRM, sales engagement, marketing automation, and data infrastructure - **Executive Contacts**: Verified email and phone for C-suite, VP of Sales, VP of Marketing, and VP of Engineering ## Use Cases ### Growth-Stage SaaS Sales Series B companies are in active replacement mode — swapping point solutions for platforms, replacing founders' tools with enterprise-grade systems. Tech stack data identifies which incumbents are in place and which categories are still unsolved. ### Revenue Tech Stack Expansion CRM, sales intelligence, revenue operations, and enablement vendors should prioritize this audience. Series B companies are formalizing their GTM motion for the first time and are evaluating vendors in every category of the modern revenue stack simultaneously. ### VC and Growth Equity Sourcing Growth equity investors and crossover funds can use this audience to identify Series B companies ahead of their Series C process. Filter by growth rate, industry, and round size to surface companies that fit specific investment theses. ### Strategic Partnership Development Series B companies are actively seeking distribution partners, technology integrations, and co-sell relationships to accelerate revenue without adding headcount. Use tech stack overlap and product category to identify natural partner candidates. ================================================================================ # Series C Companies (Last 2 Years) > 523 companies that have closed a Series C funding round within the past two years — late-growth companies with significant capital deploying enterprise-grade infrastructure at scale. **Source:** https://gtm.ai/marketplace/series-c-companies --- ## Overview Series C companies are at the inflection point between high-growth startup and enterprise — they have significant capital, proven revenue models, and are investing heavily in the systems and infrastructure needed to scale to IPO or acquisition. With 523 qualifying companies in the past two years, this is a small but extremely high-value audience of companies making major enterprise software commitments. ## What's Included Company records with confirmed Series C funding date, amount, and firmographics. These companies typically have 200–1,000 employees and are in active investment phases across sales, engineering, and operations. ## Use Cases - **Enterprise software vendors** targeting companies that have outgrown their startup-era stack - **Investment banking and M&A advisory** reaching companies beginning to think about exit paths - **Executive search firms** placing C-suite and VP-level leaders at companies in aggressive growth mode ================================================================================ # Snowflake + Databricks Stack > 6,400+ companies running both Snowflake and Databricks — data-mature organizations with dual investment in the modern data warehouse and lakehouse ecosystems. **Source:** https://gtm.ai/marketplace/snowflake-databricks-stack --- ## Overview Running both Snowflake and Databricks signals a highly sophisticated data organization — one that has invested in both structured analytics (Snowflake) and large-scale data engineering or ML (Databricks). These 6,400+ companies represent the top tier of data infrastructure maturity and are active buyers of orchestration, governance, observability, and BI tools that work across both platforms. ## What's Included Confirmed installations of both Snowflake and Databricks with company firmographics. The audience is concentrated in data-intensive industries: technology, financial services, healthcare, and media. ## Use Cases - **Data catalog and governance vendors** (Alation, Collibra) targeting organizations with complex multi-engine environments - **Orchestration platforms** (Airflow, Dagster, Prefect) managing cross-platform data pipelines - **Universal BI vendors** selling to companies querying both Snowflake and Databricks ================================================================================ # Snowflake Users with AI Intent > 13,800+ Snowflake-powered companies also showing active AI/ML research intent — organizations building AI on top of an established modern data warehouse foundation. **Source:** https://gtm.ai/marketplace/snowflake-users-with-ai-intent --- ## Overview Snowflake users who are also actively researching AI tools represent the ideal buyer for Snowflake-native AI capabilities and complementary machine learning infrastructure. These 13,800+ companies already have their data organized in Snowflake and are now looking to build AI on top of it — a high-conversion audience for anyone selling in the Snowflake AI ecosystem. ## What's Included Confirmed Snowflake installations paired with active AI/ML intent signal and composite score. Company firmographics, size, and location help prioritize high-value accounts. ## Use Cases - **Snowflake Cortex and ML vendors** reaching companies ready to operationalize AI in the data cloud - **Feature store and model management tools** targeting Snowflake-centric ML workflows - **Data labeling and annotation vendors** selling to organizations at the beginning of AI data preparation ================================================================================ # Software Engineering Leaders at Fintech Companies > VP Engineering, CTO, and Director of Engineering at financial technology companies — technical leaders building the infrastructure powering modern financial services. **Source:** https://gtm.ai/marketplace/engineering-leaders-fintech --- ## Overview Engineering leaders at fintech companies operate at the intersection of software development and financial regulation — they're building payment systems, lending platforms, trading infrastructure, and banking applications that must meet strict compliance requirements while moving at startup speed. With 8,500+ verified engineering leaders in fintech, this audience targets the technical decision-makers responsible for the modern financial services stack. ## What's Included VP Engineering, CTO, and Director of Engineering contacts at companies classified in financial technology. Direct email and phone with company firmographics for targeting by fintech segment and company size. ## Use Cases - **Compliance automation and regulatory technology** vendors targeting fintech engineering teams - **Cloud security and data encryption vendors** selling to engineering leaders managing regulated data - **API management and financial data vendors** reaching engineering leaders building connected financial services ================================================================================ # Software Engineers in Austin, TX > 6,400+ Software Engineers in the Austin metro area — verified contacts, employer, and tech stack **Source:** https://gtm.ai/marketplace/software-engineers-austin --- ## Overview This audience covers 6,400+ Software Engineers in the Austin metro area — one of the fastest-growing engineering talent markets in the US, driven by relocations from Bay Area companies, a dense startup ecosystem, and the presence of major tech employers including Dell, Oracle, Apple, and Tesla. Each record includes the engineer's current employer, seniority level, and known tech stack, enabling both recruiting and sales use cases without additional enrichment or research overhead. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and years of experience estimate - **Employer Data**: Current company name, industry, and employee count - **Tech Stack**: Known programming languages, frameworks, and tools - **Location**: Specific city, zip code, and metro area confirmation ## Use Cases ### Austin-Based Technical Recruiting Source software engineers in the Austin market for local and hybrid roles. Employer and seniority data enable recruiter outreach targeted to engineers with the right experience profile — without paying for LinkedIn Recruiter credits on every search. ### Developer Tool and Platform Sales Target software engineers in Austin at companies using your target tech stack or evaluating adjacent tools. The Austin engineering market skews toward younger companies and growth-stage employers where bottom-up developer tool adoption is common. ### Local Tech Community Outreach Build invite lists for Austin tech meetups, developer events, and local conference outreach. Employer diversity data ensures community outreach reaches engineers across the full Austin ecosystem — startups, mid-market, and enterprise. ### Coding Bootcamp and Training Programs Identify software engineers in the Austin market with shorter tenures or self-taught backgrounds who are actively investing in skill development. Years of experience and tech stack data help training programs segment by learning stage and target language or framework. ================================================================================ # Software Engineers in Chicago > 10,500+ Software Engineers in the Chicago metro area — verified contacts, employer, and tech stack **Source:** https://gtm.ai/marketplace/software-engineers-chicago --- ## Overview This audience covers 10,500+ Software Engineers in the Chicago metro area — the largest engineering talent market in the Midwest, anchored by major enterprise employers in financial services, logistics, healthcare technology, and professional services alongside a growing startup ecosystem concentrated in River North and the West Loop. Chicago engineers skew toward enterprise-grade systems, data-intensive applications, and industry-specific platforms, reflecting the region's dominant verticals. Employer industry data captures this distribution and enables vertical-specific recruiting and sales campaigns. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and years of experience estimate - **Employer Data**: Current company name, industry, and employee count - **Tech Stack**: Known programming languages, frameworks, and tools - **Location**: Neighborhood, zip code, and metro area confirmation within Chicago ## Use Cases ### Chicago-Based Technical Recruiting Source software engineers across the Chicago metro for local and hybrid roles at enterprise and growth-stage employers. Employer industry and seniority data enable precise sourcing without exhausting LinkedIn InMail credits on unqualified candidates. ### Developer Tool and Platform Sales Target software engineers at Chicago companies within your ICP. The Chicago engineering market is heavily weighted toward financial services, logistics, and healthcare tech — verticals with strong data tooling, compliance, and platform integration requirements. ### Midwest Tech Community Outreach Build invite lists for Chicago developer events, meetups, and Midwest engineering conferences. Employer diversity data ensures community outreach spans startups, mid-market, and enterprise employers rather than concentrating on one employer tier. ### Enterprise Software Team Sourcing Identify engineers at Chicago's largest enterprise employers — transportation, healthcare, financial services — for sourcing campaigns targeting enterprise software experience. Tech stack and seniority data narrow the pool to engineers with relevant platform and architecture backgrounds. ================================================================================ # Software Engineers in New York City > 24,500+ Software Engineers in the New York City metro area — verified contacts, employer, and tech stack **Source:** https://gtm.ai/marketplace/software-engineers-new-york --- ## Overview This audience covers 24,500+ Software Engineers in the New York City metro area — the second-largest engineering talent market in the US with a distinctive industry mix dominated by financial services, media and advertising, e-commerce, and a dense startup ecosystem spanning fintech, proptech, and health tech. Employer industry data is particularly valuable in New York because the engineering discipline requirements and compensation norms vary significantly across Goldman Sachs, The New York Times, and a Series B fintech startup. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and years of experience estimate - **Employer Data**: Current company name, industry, and employee count - **Tech Stack**: Known programming languages, frameworks, and tools - **Location**: Borough, zip code, and metro area confirmation within New York ## Use Cases ### NYC-Based Technical Recruiting Source software engineers across the New York metro for local, hybrid, and in-office roles. Industry and employer data enable targeted sourcing by vertical — financial services, media, or startup — matching candidate background to open role context. ### Developer Tool and Platform Sales Target software engineers at New York-based companies within your ICP. NYC employers span fintech, adtech, e-commerce, and enterprise software — each with distinct platform preferences and tooling ecosystems that tech stack data helps identify. ### Fintech and Media Tech Outreach Reach engineers at New York's dense concentration of fintech and media technology employers — two verticals where specialized developer tooling for data processing, real-time systems, and content delivery creates recurring buying cycles. ### Local Tech Community Engagement Build invite lists for NYC developer meetups, tech talks, and local engineering conferences. Seniority and employer diversity data ensure outreach reflects the full spectrum of the New York engineering community rather than only enterprise engineers. ================================================================================ # Solutions Engineers and Sales Engineers > 422,000+ Solutions Engineers, Sales Engineers, Solutions Architects, and Pre-Sales professionals — the technical champions who demo, evaluate, and close complex enterprise software deals. **Source:** https://gtm.ai/marketplace/solutions-and-sales-engineers --- ## Overview Solutions Engineers and Sales Engineers are the bridge between product and customer — they run technical evaluations, build custom demos, and own the proof-of-concept process in complex enterprise deals. With 422,000+ verified SE professionals globally, this audience spans every enterprise software vertical and is a core persona for pre-sales tooling and technical enablement vendors. ## What's Included Verified contacts with Sales Engineer, Solutions Engineer, Solutions Architect, Pre-Sales Engineer, or Presales titles. Direct email and phone with company firmographics for targeting by company type and industry. ## Use Cases - **Demo automation and pre-sales tools** (Walnut, Reprise, Navattic) targeting the SE professionals who run demos - **Technical sales content platforms** reaching SE teams building evaluation frameworks - **Recruiting firms** placing experienced SEs at enterprise software companies in growth mode ================================================================================ # Startup Founders at Series A Companies > 5,800+ founders and co-founders at Series A companies in the US — the builder-operators who own both product and commercial decisions **Source:** https://gtm.ai/marketplace/startup-founders-series-a --- ## Overview Series A founders are in the most intense operational period of company building — they've closed $5M–$15M in capital, are hiring aggressively, and are simultaneously building product and GTM for the first time at scale. These are the executives who make every vendor decision personally, move fast when they find a fit, and have the capital to spend. This audience covers 5,800+ founders and co-founders at US-based Series A companies, updated daily from funding announcements and LinkedIn signals. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **Founder Title**: CEO, Co-Founder, Founder, CTO/Co-Founder, CPO/Co-Founder, etc. - **Funding Context**: Series A round size, close date, lead investors, and total capital raised - **Company Profile**: Product category, target market, employee count, and headcount growth rate - **Stage Indicators**: Time since founding, hiring velocity, and job posting patterns - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Founder-Led Sales Motion Targeting Series A founders are the decision-maker and the evaluator in one. They respond to direct, technically credible outreach that gets to the value proposition in the first sentence — they don't have time for discovery theater. Use product category and target market to align your outreach to the founder's specific GTM challenges. Reference their funding round and headcount growth to demonstrate you understand their current stage and velocity. ### Early-Stage SaaS and Tooling Sales Series A companies are buying their foundational tech stack — CRM, marketing automation, HR platform, finance software, and data infrastructure — often for the first time with real contracts and committed budgets. Founders at this stage are price-sensitive but will pay for quality if the ROI story is clear and the implementation is fast. Lead with time-to-value and startup-friendly pricing to differentiate from enterprise vendors pitching the same category. ### VC and Accelerator Program Outreach Venture funds, accelerators, and venture studios use founder data at Series A companies to identify portfolio fit, track competitive investment activity, and recruit for advisory programs. The funding context (round size, lead investors) helps categorize companies by investor backing and valuation range. Use product category to filter by thesis alignment and employee growth rate to identify the fastest-moving companies in a cohort. ### Founder Community and Event Targeting Founder communities, CEO peer programs, and startup events use this audience to recruit participants and distribute content. Series A founders are at the stage where peer community becomes valuable — they've raised enough to have serious operational challenges but are still early enough to benefit from peer learning over hired advisory. Filter by product category and company size to build stage-appropriate cohorts for events and community programs. ================================================================================ # Startups in Denver and Boulder > 14,500+ funded startups in the Denver-Boulder metro — one of the fastest-growing startup ecosystems in the US **Source:** https://gtm.ai/marketplace/startups-denver-boulder --- ## Overview The Denver-Boulder corridor is one of the fastest-growing startup ecosystems in the United States, with particular strength in SaaS, health tech, outdoor and consumer brands, aerospace technology, and clean energy. This audience covers 14,500+ funded startups across the metro, enriched with funding stage, employee growth rate, founding date, and founder contact information. The area's talent base — fed by CU Boulder, Colorado State, and migration from coastal tech markets — has fueled a dense cluster of venture-backed companies across the Front Range. ## What's Included - **Company Identity**: Company name, website, and HQ location (Denver vs. Boulder vs. surrounding metros) - **Funding**: Stage (seed through Series C+) and total capital raised - **Size and Growth**: Employee count, year-over-year growth rate, and founding date - **Industry**: Sub-sector classification (SaaS, health tech, clean energy, aerospace, consumer) - **Contacts**: CEO, CTO, and founder contacts with verified email and LinkedIn ## Use Cases ### Colorado Startup Ecosystem Sales B2B software vendors, financial services firms, and professional services companies targeting the Colorado startup market can use this audience to build territory coverage across the Front Range — from early-stage seed companies to growth-stage Series B/C businesses approaching enterprise buying scale. ### SaaS and Tech Startup Outreach Vendors selling to startups — recruiting platforms, HR tech, financial operations, legal tech, and productivity tools — can filter by funding stage and employee count to identify accounts at the growth inflection point where infrastructure buying accelerates (typically Series A to Series B). ### Local Investor and Deal Sourcing Colorado-based VCs, angel networks, and family offices can use founding date and employee growth data to identify pre-funding or between-round companies that may be approaching their next capital raise — useful for proactive deal sourcing before formal fundraise processes begin. ### Startup Community and Event Targeting Accelerators, Techstars, co-working spaces, and startup event organizers in the Denver-Boulder area can build precise invitation and outreach lists by stage, sector, and founding year to target the most relevant companies for their programs and events. ================================================================================ # Stripe + HubSpot Stack > 20,200+ companies running both Stripe and HubSpot — the definitive PLG SaaS stack combining developer-first payments with inbound marketing and CRM. **Source:** https://gtm.ai/marketplace/stripe-hubspot-stack --- ## Overview Stripe + HubSpot is the canonical stack for product-led growth SaaS companies — Stripe powers self-serve payments while HubSpot manages marketing, inbound leads, and CRM. With 20,200+ confirmed dual installations, this audience defines the PLG SaaS segment: growth-stage companies running modern, internet-native business models that need to bridge payment events with marketing and sales workflows. ## What's Included Confirmed installations of both Stripe and HubSpot with company firmographics. The audience skews toward SMB and growth-stage SaaS companies in the 10–500 employee range operating product-led or low-touch sales motions. ## Use Cases - **Revenue automation and billing tools** connecting Stripe payment lifecycle events to HubSpot CRM workflows - **PLG analytics and activation platforms** helping companies turn free-to-paid conversion data into sales signals - **HubSpot ecosystem vendors** targeting the PLG SaaS segment of the HubSpot customer base ================================================================================ # Stripe + Salesforce Stack > 8,000+ companies running both Stripe and Salesforce — businesses operating PLG or self-serve revenue motions alongside enterprise sales, requiring payment and CRM data to be unified. **Source:** https://gtm.ai/marketplace/stripe-salesforce-stack --- ## Overview Companies running both Stripe and Salesforce are operating a hybrid go-to-market model — self-serve or PLG revenue flowing through Stripe alongside a sales-led motion managed in Salesforce. Connecting these two systems is one of the most common and painful integration challenges in modern SaaS RevOps. These 8,000+ companies are prime buyers for tools that bridge payment data and CRM pipeline data. ## What's Included Confirmed dual installations of Stripe and Salesforce with company firmographics. The audience is concentrated among growth-stage and mid-market SaaS companies that have layered enterprise sales onto a PLG or self-serve foundation. ## Use Cases - **Revenue operations and billing platforms** (Maxio, Recurly, Chargebee) connecting Stripe subscriptions to Salesforce opportunities - **PLG analytics vendors** helping companies understand how self-serve revenue converts into sales-led expansion - **CPQ and quoting tools** reaching companies that need to reconcile Stripe pricing with sales-negotiated contracts ================================================================================ # Supply Chain and Operations Leaders > VP Supply Chain, COO, Head of Operations, and VP Operations leaders — the buyers responsible for operational efficiency, logistics, and supply chain technology. **Source:** https://gtm.ai/marketplace/supply-chain-operations-leaders --- ## Overview Supply Chain and Operations leaders are responsible for some of the most significant non-IT technology investments in business — ERP systems, logistics platforms, warehouse management, procurement software, and demand planning tools. With 120,000+ verified VP and Director-level operations leaders, this audience spans manufacturing, retail, e-commerce, and distribution — the industries with the most active supply chain technology purchasing. ## What's Included Verified contacts with VP Supply Chain, VP Operations, Head of Operations, Head of Supply Chain, COO (at mid-market), or Director of Operations titles. Direct email and phone with company firmographics. ## Use Cases - **Supply chain and ERP vendors** (SAP, Oracle, NetSuite) reaching the operations leaders driving platform decisions - **Logistics and fulfillment technology** targeting VPs managing complex distribution networks - **Procurement and sourcing platforms** selling to operations leaders responsible for vendor and supply management ================================================================================ # Supply Chain Tech Companies > 9,800+ supply chain technology and logistics software companies with funding data, employee count, and executive contacts **Source:** https://gtm.ai/marketplace/supply-chain-tech-companies --- ## Overview This audience covers 9,800+ companies operating in supply chain technology and logistics software, spanning warehouse management systems, transportation management, procurement platforms, and supply chain visibility tools. Each record is categorized by functional domain and primary customer segment — 3PL, manufacturer, or retailer — enabling precise segmentation without manual research. Funding stage and total raised surface deal timing and budget signals for both sales and investment use cases. ## What's Included - **Category Classification**: Functional domain tag — WMS, TMS, procurement, visibility, or multi-category - **Customer Segment**: Primary end-market served: 3PL, manufacturer, or retailer - **Funding Data**: Stage (seed through growth/public) and total capital raised to date - **Employee Metrics**: Total headcount and year-over-year growth rate - **Executive Contacts**: C-suite and VP-level contacts with verified email and direct phone ## Use Cases ### Supply Chain Software Integration Sales Target technology leaders at supply chain software companies for API integration, data connectivity, and platform extension deals. Category classification lets you focus on adjacent categories most likely to need your integration layer. ### Logistics Platform Partnership Development Identify business development contacts at logistics and TMS platforms for co-sell, reseller, or technology partnership arrangements. Customer segment data helps align partner profiles to your own ICP. ### Enterprise Procurement and Sourcing Sales Reach procurement technology companies evaluating data, analytics, and AI tooling to enhance their platforms. Funding stage filters surface companies with recent capital and active roadmap investment. ### Investor Deal Sourcing Screen 9,800+ supply chain tech companies by funding stage, employee growth, and category to identify emerging platforms in under-served segments. Verified executive contacts accelerate introductory outreach. ================================================================================ # Talent Acquisition and Recruiting Leaders > VP, Director, and Head of Talent Acquisition leaders — the buyers responsible for recruiting technology, employer brand, and sourcing strategy at their organizations. **Source:** https://gtm.ai/marketplace/talent-acquisition-leaders --- ## Overview Talent Acquisition leaders own the recruiting technology stack — ATS, CRM, sourcing tools, background screening, skills assessments, and employer brand. They're among the most active technology buyers in HR tech, with constant pressure to improve quality-of-hire, reduce time-to-fill, and compete for talent in tight markets. With 48,000+ verified TA leaders, this audience targets the function that drives the most HR tech spend. ## What's Included Verified contacts with VP Talent Acquisition, Director of Talent Acquisition, Head of Recruiting, or Head of TA titles. Direct email and phone with company firmographics. ## Use Cases - **ATS vendors** (Greenhouse, Lever, iCIMS) reaching the TA leaders who evaluate and implement recruiting platforms - **Sourcing and candidate intelligence tools** targeting TA leaders responsible for pipeline quality - **Employer brand and recruitment marketing platforms** selling to the leaders managing talent attraction strategy ================================================================================ # Tech Companies in Chicago > 32,700+ technology and software companies in the Chicago metro — a major Midwest tech hub spanning fintech, healthtech, and enterprise software **Source:** https://gtm.ai/marketplace/tech-companies-chicago --- ## Overview Chicago has developed into the Midwest's leading technology and software hub, with particular depth in financial technology, health technology, logistics software, and enterprise B2B SaaS. The city's concentration of large enterprises across financial services, healthcare, manufacturing, and retail creates strong demand for enterprise software, and a growing venture ecosystem has produced an active pipeline of funded startups and scale-ups. This audience covers 32,700+ technology and software companies across the Chicago metro, enriched with funding data, tech stack, and key decision-maker contacts. ## What's Included - **Company Identity**: Company name, website, and Chicago metro location - **Size and Growth**: Employee count and year-over-year growth rate - **Funding**: Stage and total capital raised - **Industry Focus**: Sub-sector classification (fintech, health tech, logistics tech, enterprise SaaS, adtech, e-commerce) - **Tech Stack**: Cloud platform, known SaaS tools, and development environment - **Contacts**: Key decision-makers at VP and C-suite level with verified email and phone ## Use Cases ### Chicago Tech Ecosystem Sales B2B software vendors with Midwest territory coverage can build comprehensive account lists across Chicago's tech ecosystem — from seed-stage startups to established scale-ups — segmented by industry, size, and funding stage to prioritize accounts by deal potential and sales cycle fit. ### Midwest Enterprise Software Outreach Chicago's large enterprise base — financial services firms on LaSalle Street, major retailers, healthcare systems, and industrial companies — creates enterprise software demand that exceeds most comparable US metros outside of NYC and SF. Tech stack enrichment identifies accounts with legacy infrastructure or gaps in modern tooling. ### Chicago Fintech and Healthtech Targeting Chicago has specific depth in fintech (derivatives, trading, payments) and health tech, creating concentrated buyer clusters for specialized vendors in those verticals. Industry focus filters isolate these sub-sectors for campaigns with vertical-specific messaging and use cases. ### Local Investor and VC Deal Sourcing Chicago-based VCs, growth equity firms, and corporate venture arms can use funding stage and employee growth data to identify companies approaching the next capital raise threshold or nearing acquisition criteria — particularly relevant in a market where many companies bootstrap longer before taking institutional capital. ================================================================================ # Tech Companies in Miami > 89,800+ technology and software companies in the Miami metro area — employee count, funding data, and decision-maker contacts **Source:** https://gtm.ai/marketplace/tech-companies-miami --- ## Overview Miami has emerged as a significant US tech hub over the past several years, driven by migration from coastal tech markets, a growing LatAm diaspora founder community, and active city-level promotion of the tech ecosystem. This audience covers 89,800+ technology and software companies in the Miami metro — from early-stage startups to established software businesses — enriched with funding data, employee growth rates, and direct decision-maker contacts across the C-suite and VP layer. ## What's Included - **Company Identity**: Company name, website, and HQ address - **Size and Growth**: Employee count and year-over-year growth rate - **Funding**: Stage (seed through Series C+, PE-backed, bootstrapped) and total capital raised - **Industry**: Tech sub-sector classification (fintech, SaaS, e-commerce, crypto, health tech, logistics tech) - **Contacts**: Key decision-maker contacts including CEO, CTO, and VP-level roles ## Use Cases ### Miami Tech Ecosystem Sales Software vendors, cloud providers, and B2B tech companies targeting the Miami market can use this audience to build a complete picture of the addressable market — from startup accounts to established software businesses — with employee count and funding stage to prioritize by deal potential. ### LatAm-Focused Tech Company Outreach Miami's tech ecosystem has a strong LatAm orientation, with many companies building products for or operating in Latin American markets. This creates a distinct buyer profile relevant to payments, logistics, compliance, and cross-border commerce vendors with LatAm product offerings. ### Local Investor and VC Deal Sourcing Venture capital firms and angel investors active in the Miami tech ecosystem can use funding stage and employee growth data to identify companies approaching the next funding threshold — seed companies showing rapid growth before a Series A, or Series A companies nearing Series B criteria. ### Startup Community and Event Targeting Accelerators, co-working spaces, startup events, and ecosystem organizations targeting Miami tech companies can use this audience to build invitation and outreach lists segmented by stage, sector, and company size. ================================================================================ # Tech Startups in Toronto > 428+ funded technology startups in the Toronto metro — Canada's largest tech hub with strong AI and fintech clusters **Source:** https://gtm.ai/marketplace/tech-startups-toronto --- ## Overview Toronto is Canada's largest technology hub and home to a globally recognized AI research ecosystem anchored by the Vector Institute and the University of Toronto. The city has developed particular depth in artificial intelligence, financial technology, health technology, and enterprise SaaS, with a strong pipeline of venture-backed companies spanning seed through Series C. This audience covers 428+ funded tech startups in the Toronto metro, enriched with funding data, industry focus, and C-suite and founder contact information for the executives driving purchase decisions at each company. ## What's Included - **Company Identity**: Company name, website, and Toronto location - **Funding**: Stage (seed through Series C+) and total capital raised in CAD and USD - **Size and Growth**: Employee count, year-over-year growth rate, and founding date - **Industry Focus**: Sub-sector classification (AI/ML, fintech, health tech, e-commerce, enterprise SaaS, cleantech) - **Contacts**: CEO, CTO, and founder contacts with verified email and LinkedIn ## Use Cases ### Canadian Tech Startup Outreach B2B software vendors with Canadian GTM coverage can use this audience to build territory coverage across the Toronto startup ecosystem — identifying accounts by stage, sector, and size to prioritize outreach and assign to the appropriate AE or SDR based on deal potential. ### Toronto AI and Fintech Ecosystem Sales Toronto's concentration in AI and fintech creates a distinct buyer cluster for MLOps platforms, AI infrastructure, fintech compliance tools, and financial data products. Industry focus filters isolate AI and fintech companies for campaigns tailored to those specific buyer profiles. ### Cross-Border Canada-US Sales Motion US-headquartered vendors expanding into Canada or Canadian companies expanding into the US can use this audience to identify Toronto startups with cross-border operations, US entity presence, or investor syndicates that bridge both markets — a natural entry point for cross-border sales conversations. ### Local Investor and VC Deal Sourcing Toronto-based VCs, BDC-affiliated funds, and US investors active in the Canadian market can use funding stage and growth rate data to identify companies approaching the next funding milestone — useful for proactive outreach before formal fundraise processes begin. ================================================================================ # Unicorn Operator Alumni > 20,000+ professionals who previously worked at Stripe, Twilio, Datadog, Snowflake, or Databricks and have since moved on to new companies. **Source:** https://gtm.ai/marketplace/unicorn-operator-alumni --- ## Overview Operators who scaled through Stripe, Twilio, Datadog, Snowflake, or Databricks bring a distinctive combination of technical fluency and go-to-market rigor. This audience captures the 20,000+ professionals who built careers at these five companies and are now carrying that expertise to their next opportunity — at startups, enterprises, or ventures of their own. ## What's Included Each record is backed by verified employment history confirming tenure at one of the five unicorns. Current employer, title, job function, and direct contact information are included. Management level filtering lets you target individual contributors, managers, directors, or VPs. ## Use Cases - **Recruiting teams** seeking operators with proven hypergrowth experience - **Competitive intelligence** tracking talent flow from leading data and infrastructure companies - **Sales teams** targeting buyers who understand developer-first and product-led growth models ================================================================================ # Venture Capital Investors and Partners > General Partners, Managing Partners, and Principal-level investors at venture capital firms — the capital allocators backing the next generation of technology companies. **Source:** https://gtm.ai/marketplace/venture-capital-investors --- ## Overview Venture capital investors — from GPs at established funds to Principals at emerging managers — are the capital deployment layer of the startup ecosystem. They're evaluating companies, managing portfolios, and building networks across every sector of the technology market. With 85,000+ verified VC professionals, this audience is essential for anyone selling into the venture capital industry or building founder-investor relationships. ## What's Included Verified contacts at venture capital firms with investor-level titles (GP, Managing Partner, Partner, Principal, Associate). Direct email and phone with firm firmographics for targeting by fund size, stage focus, and geography. ## Use Cases - **Portfolio management and deal flow platforms** selling to VC firms managing large investment portfolios - **LP reporting and investor relations tools** reaching partners responsible for fund administration - **Founders** building investor relationship pipelines in relevant sectors and geographies ================================================================================ # VP and Head of Product > 96,700+ Chief Product Officers, VP of Product, and Head of Product contacts — the executive product buyers responsible for roadmap, tooling, and customer discovery investments. **Source:** https://gtm.ai/marketplace/vp-and-head-of-product --- ## Overview VP Product and Head of Product are the primary buyers for product management platforms, customer research tools, analytics, and experimentation infrastructure. With 96,700+ verified product leaders globally, this audience spans the full range — from startup Heads of Product running lean with one tool to enterprise CPOs managing large PM organizations and complex tooling ecosystems. ## What's Included Verified contacts with CPO, Chief Product Officer, VP of Product, or Head of Product titles. Direct email and phone with company firmographics for filtering by company stage, size, and industry. ## Use Cases - **Product management platforms** (Productboard, Aha!, Linear) targeting their primary buyer - **Customer research and feedback tools** (UserTesting, Pendo, FullStory) selling to product leaders - **A/B testing and experimentation platforms** reaching the product leaders who own conversion and growth ================================================================================ # VP Engineering at Snowflake + Databricks Companies > 33,800+ VP-level engineering leaders at companies running both Snowflake and Databricks — the most data-mature engineering organizations, building at the intersection of analytics and ML. **Source:** https://gtm.ai/marketplace/vp-engineering-snowflake-databricks --- ## Overview VP Engineering leaders at companies running both Snowflake and Databricks are at organizations with the most sophisticated data engineering infrastructure in the market. These companies have invested in both the warehouse (Snowflake) and the lakehouse/ML layer (Databricks) — and their engineering leaders are evaluating the next layer of the stack: orchestration, observability, feature management, and AI tooling. This 33,800+ audience targets precisely that buyer profile. ## What's Included VP-level engineering contacts at companies with confirmed dual Snowflake and Databricks installations. Direct contact details and company firmographics included. ## Use Cases - **Data engineering and AI infrastructure vendors** targeting VPs at organizations with mature multi-platform data stacks - **Orchestration and pipeline vendors** (Dagster, Prefect, Airflow managed services) reaching engineering decision-makers - **ML platform and LLMOps vendors** targeting engineering leaders building AI on modern data foundations ================================================================================ # VP Engineering at Workday-Powered Companies > 45,900+ VP-level engineering leaders at companies running Workday — organizations at the scale where engineering and HR infrastructure investments align. **Source:** https://gtm.ai/marketplace/vp-engineering-at-workday-companies --- ## Overview VP Engineering leaders at companies running Workday are at organizations with enough scale and maturity to have invested in enterprise HR infrastructure — a proxy for companies with large engineering teams, significant engineering budgets, and structured people processes. With 45,900+ verified records, this audience is a strong signal for recruiting platforms, engineering tooling, and productivity vendors targeting larger engineering organizations. ## What's Included VP-level engineering contacts at companies with confirmed Workday installations, including direct contact details and company firmographics. ## Use Cases - **Technical recruiting platforms** reaching VP Engineering at companies with substantial engineering headcount - **Developer productivity and collaboration tools** targeting engineering leaders at scaled organizations - **Engineering analytics and planning tools** selling to VPs managing large, complex engineering functions ================================================================================ # VP Sales at Gong-Powered Companies > 2,800+ VP-level sales leaders at companies with confirmed Gong installations — decision-makers who already speak the language of revenue intelligence and conversation data. **Source:** https://gtm.ai/marketplace/vp-sales-at-gong-companies --- ## Overview VP Sales leaders at Gong-powered companies have a specific profile: they're data-driven, they review call recordings, and they've invested in conversation intelligence as a coaching and forecasting input. With 2,800+ verified records, this audience targets the most analytically sophisticated sales leaders in the market — those already operating at a level where complementary tools get evaluated seriously. ## What's Included VP-level sales contacts with confirmed employment at companies running Gong, including direct contact details and company firmographics. ## Use Cases - **Sales forecasting and pipeline analytics** vendors targeting revenue leaders already using AI for deals - **Enablement platforms** selling coaching solutions to Gong-familiar VP Sales - **Sales compensation and quota management tools** targeting data-driven revenue organizations ================================================================================ # VPs of Engineering at Tech Companies > 37,800+ VPs of Engineering at software and technology companies — verified contacts, team size, and engineering stack **Source:** https://gtm.ai/marketplace/vps-of-engineering-tech-companies --- ## Overview This audience covers 37,800+ VPs of Engineering at software and technology companies — the operational leaders who own engineering headcount, tooling budgets, and infrastructure decisions below the CTO. At most tech companies, the VP of Engineering is the primary buyer for developer tooling, cloud management, and team productivity platforms. Engineering team size and known tech stack data enable sellers to lead with specific, relevant value propositions rather than generic discovery questions. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, reporting structure, and tenure at current company - **Company Data**: Company name, revenue range estimate, and industry classification - **Engineering Team**: Estimated team headcount by function (frontend, backend, infra, data) - **Tech Stack**: Known development, infrastructure, and operations tooling ## Use Cases ### Developer Platform and Tooling Sales Target VPs of Engineering who control tooling budgets at tech companies. Engineering team size filters help identify organizations at the scale threshold where platform standardization and developer experience tooling become budget priorities. ### Cloud Infrastructure and DevOps Sales Reach engineering leaders responsible for cloud spend, infrastructure architecture, and DevOps platform decisions. Tech stack data surfaces existing cloud provider relationships and identifies displacement and expansion opportunities. ### Engineering Recruiting and Staffing Source VPs of Engineering actively scaling their teams. At tech companies, the VP of Engineering is typically the hiring manager for senior IC and staff engineer roles — direct contact data reduces dependency on expensive job board advertising. ### Technical Training and Certification Identify engineering leaders at companies investing in upskilling programs, cloud certifications, and internal learning platforms. Team size and growth rate data indicate organizations with active L&D needs. ================================================================================ # VPs of Finance at Public Companies > 23,500+ VPs of Finance at US-listed public companies — verified contacts, company financials, and reporting structure context **Source:** https://gtm.ai/marketplace/vps-of-finance-public-companies --- ## Overview This audience covers 23,500+ VPs of Finance at US-listed public companies — the finance leaders below the CFO who manage FP&A, reporting, treasury, and operational finance functions with direct influence over tool and service vendor decisions. Public company context — ticker, revenue, analyst coverage, and institutional ownership — differentiates this audience from standard VP of Finance lists and enables segmentation by company financial profile and market cap tier. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, functional scope (FP&A, treasury, corporate finance), and tenure - **Company Financials**: Revenue, market cap estimate, ticker, and exchange - **Reporting Context**: Finance team structure and CFO reporting relationship - **Capital Markets Data**: Sell-side analyst coverage count and institutional ownership percentage ## Use Cases ### Financial Software and Reporting Tools Reach VPs of Finance at public companies evaluating financial planning, consolidation, and external reporting platforms. Public company reporting requirements — 10-K, 10-Q, Reg FD compliance — create recurring software evaluation cycles that this audience directly targets. ### Audit and Advisory Services Target senior finance leaders at public companies for audit, tax advisory, and internal controls consulting. Market cap and revenue filters allow audit firms to segment by engagement complexity and fee range. ### Investor Relations and Capital Markets Identify finance VPs with investor relations responsibilities at public companies for IR platforms, earnings call technology, and capital markets advisory services. Analyst coverage data provides context on the IR complexity each company manages. ### Treasury and Risk Management Solutions Reach VPs of Finance responsible for treasury operations, cash management, and financial risk at public companies. Revenue and employee count filters help identify organizations where treasury management tooling is likely to be actively evaluated. ================================================================================ # VPs of HR at Companies with 500+ Employees > 43,900+ VPs of Human Resources at companies with 500 or more employees — verified contacts, org size, and HR tech stack **Source:** https://gtm.ai/marketplace/vps-of-hr-large-companies --- ## Overview This audience covers 43,900+ VPs of Human Resources at companies with 500 or more employees — the threshold where HR technology platforms, benefits administration, and structured talent acquisition programs become operationally necessary. Known HR tech stack data across ATS, HRIS, and payroll categories enables sellers to identify incumbent vendors, renewal windows, and displacement opportunities before the first outreach. HR team size estimates provide context on the operational scope each VP manages. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, functional scope, and tenure at current company - **Company Data**: Company name, employee count, industry, and HQ location - **HR Tech Stack**: Known tools across ATS, HRIS, payroll, L&D, and benefits administration - **Team Estimates**: HR team headcount estimate relative to total company size ## Use Cases ### HRIS and HR Tech Platform Sales Target HR VPs at companies with 500+ employees who are actively managing or evaluating core HR systems. HRIS, payroll, and benefits tech stack data reveals incumbent vendors and signals where modernization cycles are underway. ### Benefits and Total Rewards Solutions Reach HR leaders at mid-to-large companies responsible for health benefits, equity programs, and total compensation strategy. Employee count filters ensure targeting is focused on organizations with the scale to justify benefits platform investment. ### HR Consulting and Advisory Identify VP-level HR leaders at companies navigating organizational restructuring, compensation benchmarking, or compliance program development. Tenure data surfaces recently hired HR VPs who are most likely to evaluate new advisory relationships. ### Recruiting and Talent Acquisition Services Source HR VPs who own talent acquisition budgets at companies actively scaling headcount. Employee count and growth rate data identify organizations with the highest volume recruiting needs where RPO, executive search, and ATS tools provide immediate value. ================================================================================ # VPs of IT at Mid-Market Companies > 35,000+ VPs and Directors of IT at companies with 200–2,000 employees — the primary technology buyer at mid-market organizations **Source:** https://gtm.ai/marketplace/vp-it-mid-market --- ## Overview At mid-market companies (200–2,000 employees), the VP or Director of IT is typically the primary technology buyer — they own the IT budget, evaluate vendors, and often make final purchase decisions without a formal IT procurement layer. This audience covers 35,000+ VP and Director of IT contacts globally, with IT environment context, tech stack signals, and budget authority indicators to support precise targeting. Mid-market IT buyers are faster to evaluate and close than enterprise buyers, with fewer stakeholders and shorter procurement cycles. ## What's Included - **Contact Identity**: Full name, LinkedIn profile URL, and verified contact information - **Title and IT Scope**: Full title (VP of IT, Director of IT, IT Director, VP of Information Technology) and scope indicators (infrastructure, security, helpdesk, application management) - **Company Profile**: Company name, employee count (200–2,000 range), revenue tier, and industry classification - **IT Environment**: Known endpoint management, cloud platform, collaboration tools, and network infrastructure from technographic signals - **Budget Authority**: Employee count and company revenue as proxy for IT budget range; IT team size signals where available - **Verified Contact Data**: Work email and direct phone with confidence scoring ## Use Cases ### Mid-Market IT Infrastructure Sales Hardware vendors, network infrastructure providers, and on-premise/hybrid storage platforms target VP of IT contacts at mid-market companies as their primary buyer. Mid-market IT leaders evaluate infrastructure solutions based on total cost of ownership, ease of management with small IT teams, and vendor support quality — not enterprise procurement criteria. Use employee count to segment between lower mid-market (200–500) and upper mid-market (500–2,000) to align your pricing and support model. ### Cybersecurity and Endpoint Protection Mid-market companies are the fastest-growing cybersecurity market segment — large enough to have real risk exposure but too small for dedicated security teams. VP of IT contacts at mid-market companies are simultaneously the IT director and the de facto CISO. Endpoint detection, email security, identity management, and security awareness training vendors should lead with ease of deployment and managed options for lean IT teams. ### Cloud and SaaS Platform Sales Mid-market IT leaders are the primary buyers of cloud infrastructure, SaaS platform contracts, and IT management tools. They evaluate Microsoft 365 vs. Google Workspace, AWS vs. Azure vs. GCP, and MDM and endpoint management platforms. Use IT environment signals to identify companies still on legacy on-premise infrastructure and those already in cloud-first environments — these represent very different sales motions and product fits. ### Managed Services and Outsourcing MSPs, IT outsourcing firms, and co-managed IT services target VP of IT contacts at mid-market companies where IT teams are understaffed relative to the company's infrastructure complexity. Mid-market IT leaders are the most receptive segment for co-managed services — they have the complexity of an enterprise without the headcount to manage it internally. Filter by IT team size signals and industry to prioritize the highest-fit targets for your MSP model. ================================================================================ # VPs of Marketing at B2B Companies > 116,000+ VPs of Marketing at B2B companies — verified contacts, company size, marketing tech stack, and budget signals **Source:** https://gtm.ai/marketplace/vps-of-marketing-b2b --- ## Overview This audience covers 116,000+ VPs of Marketing at B2B companies across all sizes and verticals — the layer of marketing leadership that owns program budgets, agency relationships, and technology decisions when a CMO is not present or is focused on strategy. Each record includes the company's B2B classification, known marketing tech stack, and revenue range, enabling sellers and agencies to segment by organizational maturity and likely spend capacity. At companies under 500 employees, the VP of Marketing is frequently the top marketing decision-maker. ## What's Included - **Contact Profile**: Full name, LinkedIn URL, verified email, and direct phone number - **Role Context**: Current title, seniority level, and tenure at current company - **Company Data**: Company name, employee count, revenue range, and B2B classification - **Marketing Tech Stack**: Known tools across MAP, ABM, content, social, and analytics - **Budget Signals**: Funding events, employee growth rate, and tech stack recency ## Use Cases ### MarTech Platform Sales Reach B2B marketing VPs who own the platform evaluation and renewal cycle for marketing automation, ABM, and analytics tools. Tech stack data identifies existing vendor relationships and surfaces companies with tooling gaps or aging contracts. ### Demand Gen and Media Agency Outreach Target VPs of Marketing who control agency relationships and media budgets at B2B companies. Company size and revenue range filters help agencies focus on accounts within their addressable billing range. ### Content and SEO Tool Sales Identify marketing leaders at B2B companies with active content and organic growth programs. Employee count and tech stack signals indicate companies investing in content-led demand generation where SEO and content tooling are budget priorities. ### Marketing Consulting and Advisory Source leads for marketing strategy consulting, fractional CMO services, and go-to-market advisory at B2B companies where the VP of Marketing is the senior-most marketing leader. Revenue and headcount data help identify the organizations most likely to engage outside expertise.