Intent data has crossed from "interesting emerging category" to something close to standard practice for serious B2B go-to-market teams. Roughly three-quarters of high-performing sales and marketing organizations now use some form of intent data in their process. That's a real shift — but it's also created a gap between teams that buy intent data and teams that actually get value from it, and that gap is bigger than most vendors will admit.
What intent data actually is
Intent data is behavioral evidence that a person or company is actively researching a problem or solution category — before they've filled out a form or raised their hand in any obvious way. It generally comes from three sources:
- First-party intent. Activity on your own properties: website visits, pricing page views, content downloads, product trial usage. This is the highest-quality signal because the person chose to interact with you directly, but it's also the sparsest — most of your future buyers haven't visited your site yet.
- Third-party intent. Research behavior happening elsewhere that intent providers can observe at scale, like content consumption on industry sites or review platforms.
- AI-inferred signals. Patterns pulled from a combination of sources, including things like hiring activity (a company posting several SDR roles is a signal they're investing in outbound, and might need related tools) or job changes (a champion who advocated for your product moving to a new company).
Where it actually helps
Timing outreach. The core value of intent data isn't finding new companies to target — it's knowing when to reach an account you already had on a list. Contacting an account while they're actively researching, instead of on a random quarterly cadence, is the difference between a call that lands and one that doesn't.
Prioritizing your target list. Not every account on your ICP list is in-market right now. Intent signals help separate "fits our profile" from "fits our profile and is actually looking," which is a much more useful list to work from.
Sharpening ABM campaigns. Activating personalized, higher-effort campaigns only for accounts already showing intent signals improves the return on that effort, instead of spending the same personalization budget evenly across accounts that are and aren't ready.
Where teams waste money on it
Treating every signal as equal. Not all buying signals predict an actual purchase. A company researching your category broadly isn't the same as a company that just visited your pricing page three times this week. Vendors sell aggregate intent scores that blur this distinction — it's worth asking exactly what behaviors are behind any score before acting on it.
Buying it and never operationalizing it. Intent data that sits in a dashboard nobody checks daily isn't a signal — it's a subscription fee. The teams getting real value have built a workflow where a qualifying signal automatically triggers a specific next action: an alert to a rep, a campaign activation, a change in ad targeting.
Ignoring attribution difficulty. Even with good intent data, tying revenue directly back to "we acted on this signal" remains genuinely hard. Don't expect a clean ROI number in month one — expect a gradual improvement in conversion rates and outreach efficiency instead.
The practical starting point
You don't need an enterprise intent platform to start. Even simple first-party signals — who's visiting your pricing page repeatedly, who's opened every email in a sequence, who suddenly went quiet after months of engagement — are intent data you likely already have and aren't using systematically. Start there, build the habit of acting on signals fast, and layer in third-party or AI-inferred data once the workflow around it actually works.
