Intent data is behavioral information that indicates when a company is actively researching a product or service category. When employees at Acme Corp start reading articles about "CRM alternatives," visiting vendor comparison pages, and downloading buyer's guides about sales technology, that pattern of behavior signals purchasing intent before anyone at Acme fills out a contact form or requests a demo. For B2B sales teams, intent data answers the question that CRM data alone can't: which of your target accounts are actively in a buying cycle right now?
TL;DR: Intent data tracks anonymous B2B research behavior across the web to identify companies showing above-baseline interest in topics related to your solution. It comes in two forms: first-party (activity on your own website and content) and third-party (activity across external websites and content networks). Sales teams use intent data to prioritize outreach, time engagement, and personalize messaging. Intent data is most effective when combined with verified account signals (leadership changes, funding, hiring) that explain why the intent exists.
How Intent Data Works
First-Party Intent Data
Intent data comes from three sources, each with different coverage and reliability tradeoffs.
First-party intent comes from your own digital properties:
- Website visits: Which companies are visiting your site, which pages they view, and how often they return. Tools like Clearbit Reveal and 6sense identify the companies behind anonymous website traffic using IP-to-company matching and cookie-based identification.
- Content engagement: Downloads of white papers, attendance at webinars, engagement with blog posts, and interactions with email campaigns.
- Product interaction: Free trial signups, demo requests, pricing page views, and feature page exploration.
Strength: High accuracy. A company visiting your pricing page three times in a week is demonstrating clear intent. You know they're interested in your specific solution, not just the category.
Limitation: Narrow scope. First-party data only captures companies that have already found you. It misses the larger population of companies researching your category on other websites.
Third-Party Intent Data
Third-party intent comes from external sources:
- Content consumption: Tracking which companies read articles, download reports, and engage with content across networks of B2B websites. Bombora's cooperative network of 5,000+ sites is the largest example.
- Review site activity: Platforms like G2 and TrustRadius track which companies research, compare, and review products in your category.
- Search behavior: Some providers track keyword searches and website visits at the company level (typically through IP matching and reverse DNS).
Strength: Broad coverage. Third-party data captures companies researching your category before they find your website, giving you an early signal of buying interest.
Limitation: Lower accuracy. Anonymous browsing behavior can be triggered by non-buying activities: employees doing competitive research, writing content, conducting academic research, or simply browsing out of curiosity. Not every "surge" in topic research represents a genuine buying evaluation.
How Sales Teams Use Intent Data
Use Case 1: Account Prioritization
The most common application. Instead of treating all target accounts equally, intent data identifies which accounts are actively researching your category, allowing reps to focus outreach on accounts with demonstrated interest.
Before intent data: A rep with 200 target accounts contacts them based on firmographic priority (industry fit, company size) or random selection. Most outreach reaches accounts with no current buying need.
With intent data: The same rep sees that 30 of their 200 accounts are showing intent surges for relevant topics this week. Outreach focuses on those 30 accounts, dramatically improving the odds of reaching someone who's actually considering a purchase.
Typical improvement: Teams using intent-based prioritization report 2-3x higher response rates on outbound outreach compared to non-intent-informed outreach.
Use Case 2: Timing Outreach
Intent data reveals when an account enters a buying cycle, allowing reps to engage at the optimal moment rather than guessing.
The timing advantage: A company researching "sales intelligence tools" this week is receptive to a relevant outreach today. The same company six months ago — before the research started — would have ignored the same message. Intent data collapses the gap between when a company starts evaluating and when a rep reaches out.
Use Case 3: Personalizing Messaging
Knowing what topics an account is researching enables more relevant outreach than generic value propositions.
Without intent: "Hi, I'd like to introduce our sales intelligence platform."
With intent: "I noticed your team has been researching account intelligence solutions this month. We help companies like [reference customer] solve [specific problem relevant to the topics being researched]. Would it be worth a conversation?"
The second message isn't dramatically different, but it demonstrates awareness that the prospect is in an active evaluation, creating relevance that cold outreach lacks.
Use Case 4: Competitive Intelligence
Intent data reveals when target accounts are researching your competitors specifically. This triggers competitive displacement outreach and helps reps prepare for competitive objections before discovery calls.
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The Limitations of Intent Data Alone
Intent data is a powerful signal, but it has meaningful limitations that sales teams should understand:
Anonymous by Default
Third-party intent data identifies the company but rarely the specific person doing the research. Knowing that "someone at Acme Corp" is researching sales tools doesn't tell you whether it's the VP of Sales (a buyer) or a marketing intern writing a blog post (not a buyer). This anonymity means reps still need to identify the right stakeholders through other sources.
Topic-Level, Not Solution-Level
Most intent data operates at the topic level: "sales intelligence," "CRM tools," "revenue operations." It doesn't tell you whether the account is researching your specific solution, a competitor, or the category in general. A company showing intent for "sales intelligence" might be evaluating ZoomInfo, Salesmotion, or simply trying to understand what the category means.
False Positives
Industry estimates suggest that 30-50% of third-party intent signals represent non-buying behavior: competitive research, content creation, employee education, or academic interest. Teams that treat every intent signal as a buying signal waste outreach effort on accounts that aren't actually evaluating vendors.
Missing Context
Intent data tells you that an account is researching. It doesn't tell you why. A company might be researching "sales tools" because they're evaluating vendors (good), because their new VP of Sales asked for a category overview (also good), or because their intern is writing a comparison article (not useful). Without additional context — who's researching, what event triggered it, and what the company's strategic priorities are — intent data provides direction without specificity.
Combining Intent Data with Account Signals
The most effective teams don't use intent data in isolation. They combine it with verified account intelligence signals that provide the context intent data lacks:
| Signal Type | What It Tells You | Combined with Intent |
|---|---|---|
| Leadership change | A new decision-maker just joined | "New VP Sales + intent surge for your category = high-priority outreach" |
| Earnings priorities | What the company's leadership is focused on | "CEO mentioned 'operational efficiency' + intent for your tool category = strong fit signal" |
| Hiring surge | The company is investing in a specific function | "Hiring 15 sales reps + researching sales tools = scaling and buying" |
| Funding event | The company has capital to invest | "Series C funding + intent surge = budget available for new tools" |
| Buying signals | Verified events indicating purchase readiness | Intent confirms the timing; signals provide the context for personalized engagement |
Teams like Frontify achieved 42% sales velocity improvement by combining signal-driven account intelligence with intent-based prioritization, ensuring that reps engaged accounts showing both verified buying events and active research behavior.
“The account and contact signals are key for reaching out at important times, and the value-add messaging it creates unique to every contact helps save time and efficiency.”
Daniel Pitman
Mid-Market Account Executive, Black Swan Data
Key Takeaways
- Intent data tracks anonymous company-level research behavior to identify accounts showing above-baseline interest in your solution category. It comes in first-party (your website) and third-party (external web activity) forms.
- The four primary use cases are account prioritization (which accounts to focus on), timing (when to engage), personalization (what to say), and competitive intelligence (who else they're evaluating).
- False positive rates of 30-50% on third-party intent data mean that not every signal represents real buying intent. Validate intent with additional data before committing significant rep effort.
- Intent data identifies general interest. Account signals (leadership changes, earnings priorities, hiring, funding) explain why the interest exists and provide context for relevant outreach.
- First-party intent (website visits, content engagement) is more accurate than third-party intent but narrower in coverage. Use both for the most complete picture.
- Start with one intent data source, measure signal-to-meeting conversion, and expand coverage only after proving that existing intent data is being acted on effectively.
Frequently Asked Questions
What is intent data in B2B sales?
Intent data is behavioral information that indicates when a company is actively researching products or services in your category. It tracks anonymous web browsing patterns, content consumption, and search behavior at the company level to identify accounts showing above-baseline interest in relevant topics. B2B sales teams use intent data to prioritize outreach to accounts actively evaluating solutions rather than prospecting blindly across their entire target account list.
What is the difference between first-party and third-party intent data?
First-party intent data comes from activity on your own digital properties: website visits, content downloads, email engagement, and product interactions. Third-party intent data comes from external sources: browsing behavior across content networks, review site activity, and search patterns tracked by B2B intent data providers. First-party data is more accurate (the prospect engaged with your brand specifically) but narrower. Third-party data is broader (captures category-level research) but less accurate due to anonymous tracking and false positives.
How accurate is intent data?
Accuracy varies by source. First-party intent (website and content engagement) is 70-90% accurate for indicating genuine interest. Third-party cooperative data (Bombora) is estimated at 40-60% actionable, with the remainder representing non-buying activity. Platform-specific intent (G2, TrustRadius) falls between, at 50-70% accuracy, because the behavior (visiting a software review site) is more indicative of buying intent than general topic browsing. Combining multiple intent sources with verified account signals improves effective accuracy to 60-80%.
Is intent data worth the investment for small sales teams?
For teams with fewer than 10 reps and limited target accounts (under 500), standalone third-party intent data may not justify the $25,000-75,000 annual cost. Start with free or lower-cost first-party signals (website visitor identification, content engagement tracking) and platform-specific intent (G2, LinkedIn). As the team scales and the target account list grows, third-party intent data becomes more valuable for identifying buying activity across a larger universe of accounts.
