Intent data is information about what a company is actively researching at a specific moment. Unlike firmographic data (who a company is) or behavioral data (what they’ve done in the past), intent data is about what they’re doing now. According to 6sense research, 94% of B2B buyers use LLMs in vendor research, and 61% of the buying journey happens before first contact with your organization. A company is researching marketing automation solutions.
A company is intensely reading about revenue attribution. A company is searching for “how to evaluate demand generation tools.” Intent data captures that present moment of research and evaluation. When you execute effective demand generation, you’re creating awareness and education increasing the number of people researching your category. When you use intent data effectively, you’re prioritizing those already researching.
Combining both. Massively increasing researchers through demand generation while simultaneously identifying most likely researchers through intent data. Builds a demand generation system powerful at scale.
Intent data has two primary types: first-party intent data and third-party intent data. First-party intent data is behavior observed on your own properties. Site, email, content, community. If someone visits multiple site pages, downloads whitepapers, attends webinars, intensely consumes content, they’re showing intent.
First-party intent value is it’s true and specific. You see exactly what person is doing what. The challenge is it only captures traceable journey portion. Most people aren’t visiting your site when conducting initial research.
They’re researching Google, Slack, internal company portals, consultant recommendations. All that’s outside your line of sight.
Third-party intent data is where power becomes extraordinary. Companies like Bombora, 6sense, Clearbit, and Demandbase are capturing research signals from hundreds of thousands of websites, applications, and communities. When you see spikes in “marketing automation pricing comparison” searches, that’s intent signal. When someone visits analyst reviews of solutions, that’s intent.
When there’s traffic increase to keywords related to your category from a specific company, that’s intent. These providers connect that intent to company and contact data, enabling marketers to know: “Company ABC is actively researching [your category] solutions.” That’s extraordinarily powerful because it’s true signal they’re in market, not assumed based on firmographics.
Disadvantage of third-party intent data is it’s not brand-specific. You know company ABC is researching demand generation, but you don’t know if they’re considering your solution or competitors. However, that doesn’t matter for demand generation purposes. If a company is researching, you’re in a time window where they’re especially receptive to messages on that topic.
It’s the moment for increasing outreach, distributing relevant content, inviting to webinars, offering demos. Teams using intent data sophisticatedly are seeing 2-3x lift in conversion rates when applied because they’re touching people at maximum receptivity moments.
Applying intent data to demand generation is through dynamic personalization and targeting. If intent data shows company ABC is researching revenue attribution, your ads to people at ABC become about attribution. Content you promote changes. Email outreach emphasizes attribution.
Website experience personalizes to show attribution-focused copy. From prospect perspective, it seems you know exactly what they’re researching because you actually do. This is opposite of interruption marketing. It’s precisely targeted marketing based on real signals.
Challenge with intent data is many teams over-optimize hot signals and under-optimize early-stage signals. Intent data typically categorizes signals as “hot” (very clear active research) or “warm” (research but not urgency). Hot signals are valuable but represent small market portion. Most TAM isn’t researching at any given moment.
However, your demand generation should focus on creating warm signals, not just reacting to hot signals. If you only touch hot signals, you’re leaving enormously on table. The company beginning research weeks before hot signals is exactly where intensive demand generation can create advantage. Then when they enter hot signal status, they’re already familiar with brand because they’ve been consuming content in dark funnel.
Integration of intent data with ABM is where explosive effectiveness emerges. You have a list of 500 accounts that are 1:Few targets based on ICP fit. Monthly, you overlay intent data on that list. The 20 accounts currently showing hot intent get 1:1 personalized attention, event invitations, executive engagement, sales outreach.
The 50 accounts showing warm intent get 1:Few segmented campaigns with more targeted messaging. The 430 showing no current intent continue receiving general demand creation. When an account moves from no-intent to warm to hot, your engagement changes in real time. This ensures you’re always optimizing engagement level to current context, not treating people uniformly.
Finally, intent data has implications for demand capture budget. If you know where active intention exists, you can invest demand capture more efficiently. Direct ad spend and email outreach toward accounts showing intent. Retarget people actively researching.
Less waste on cold audiences. Teams adopting intent data typically see 20-30% reduction in CAC because they’re converting people at maximum receptivity moments.