What is buyer intent data?

Intent data platforms track online behavior across vast networks of content publishers, review sites, and media properties. The most established third-party model is a data cooperative: Bombora, for example, places a proprietary JavaScript tag on member publisher sites and captures page views, downloads, and engagement — all based on user consent. That raw behavior is then processed against a baseline consumption rate established for each company on each topic cluster.

When a company's employees consume a topic — say, 'zero trust security' or 'revenue intelligence' — at a rate significantly above that baseline, across more employees, more content types, and more days than usual, the platform flags the account as surging. Weighting factors include content type (a vendor comparison page outweighs a blog post), time on page, scroll depth, and the number of distinct individuals engaging. The result is a topic-level intent score, not a single keyword match — which is more resilient to noise but introduces some abstraction from the underlying behavior.

This scored, account-level signal is matched to firmographic data and delivered to your CRM or sales engagement platform — typically as a weekly or real-time feed of surging accounts ranked by signal strength and ICP fit. Bombora's co-op covers more than 5,500 B2B media sites and captures 17.6 billion monthly interactions across nearly 4.8 million unique domains. 86% of data in the co-op is exclusively shared with Bombora — meaning competitors can't access the same signals from the same sources.

First-party intent data is behavior you observe directly on your own properties: which pages prospects visit, how long they spend on your pricing page, what they download, what they click in your emails, and how they use your product. Because you collected it yourself and have direct context, it has the highest signal quality. Its limitation is coverage: it only captures accounts already engaging with you.

Second-party intent data comes from a specific external partner that shares its first-party data directly with you. G2 Buyer Intent is the primary example — when someone at a target account views your G2 profile or browses your software category, G2 surfaces that signal. Second-party data has strong quality because it's direct behavioral data from a known source, and it's particularly valuable for identifying late-stage evaluators who haven't yet visited your site.

Third-party intent data is aggregated from networks of external publishers and sold to multiple buyers — including, potentially, your competitors. Providers like Bombora operate consent-based co-ops; others pull signals from programmatic advertising bidstreams (the real-time ad auction infrastructure). Bidstream-sourced data has much broader reach but weaker privacy footing: the UK Information Commissioner's Office and the Belgian Data Protection Authority have both ruled that collecting intent data through RTB bidstreams violates GDPR, and the Court of Justice of the European Union confirmed the Belgian DPA's analysis. Best practice is to layer all three: use third-party signals to identify in-market accounts you haven't touched, second-party signals to catch late-stage evaluators, and first-party signals to confirm and prioritize once they engage.

The headline metrics look compelling. Ninety-six percent of B2B marketers reported achieving their program goals when using intent data, according to a study conducted by Rollworks and Bombora. Research by Mixology Digital found 93% of B2B marketers see higher conversion rates, and 82% say sales teams convert intent-based leads faster than cold leads. NetSPI, a security firm, grew pipeline opportunities 61% by layering ZoomInfo intent signals into its demand-generation motion (ZoomInfo case study). Palo Alto Networks used ZoomInfo intent data and Scoops to uncover 1,500+ net-new accounts previously invisible to their enterprise sales team.

But the data also has well-documented limitations. Coverage is thinner in niche verticals and among smaller companies. IP-address-based account identification — used by many providers — is noisy: shared office IPs, VPNs, and remote workers can attribute research to the wrong company. A topic spike doesn't prove purchase intent: a company's team reading about 'zero trust security' might be doing competitive benchmarking, not shopping. And implementation friction is real: DemandScience research found 61% of teams using intent data take up to six months to realize ROI, and many exceed implementation budgets. A 2026 survey found only 24% of intent data users report exceptional ROI — the majority see moderate value, with the gap between expectation and outcome driven largely by data quality, routing speed, and rep adoption.

The practical lesson: intent data works best as a prioritization and triggering layer on top of a well-defined ICP, not as a standalone lead source. It helps you work smarter on accounts that already fit; it doesn't replace qualification.

The most common use case is account prioritization. Each week — or in real time if the tech stack supports it — SDR teams pull accounts surging on their core topic clusters, cross-reference against ICP criteria (company size, industry, technographic fit), and move matches to the top of the outreach queue. The intent signal becomes both the reason to reach out and the personalization hook: a rep can credibly open with context about the research category rather than a cold pitch, and that relevance drives higher reply rates.

Marketing teams use intent data to concentrate ad spend on accounts showing active research, rather than cold lookalikes. ABM teams use it to trigger content syndication, direct mail, and event invitation sequences timed to the buying window. RevOps teams integrate intent scores into lead scoring models so MQL thresholds reflect buying stage, not just engagement depth.

Speed is the critical execution variable. Research from Google and the Corporate Executive Board, widely cited alongside InsideSales data, finds that 35–50% of B2B deals go to the first vendor to respond. Intent data creates the window; speed wins it. Teams that route intent alerts to reps within hours consistently outperform those that batch-process weekly feeds. This requires tight routing rules — automated Slack notifications when accounts surge, direct CRM task creation, and rep training on how to use the signal rather than ignore it.

Komo treats buyer intent data as one of several signal layers it monitors across your target accounts — alongside job changes, funding rounds, hiring velocity, and technographic shifts. When an account surges on a topic cluster relevant to your category, Komo's engine fires: it researches the account, identifies the contacts most likely to own the problem the intent signal points to, and drafts a relevant, personalized outreach message that leads with the signal as the reason to reach out now.

The difference from a raw intent feed piped into an automated sequence is the human checkpoint. Komo handles detection, research, and drafting — the repetitive work that lives between your intent data subscription and your rep's inbox — but keeps you on every send that matters. You review the draft, see the reasoning behind it, and decide whether to send or adjust. That design means the timing advantage of intent signals (acting when an account is genuinely in-market) doesn't come at the cost of message quality, rep judgment, or deliverability.

For teams that already subscribe to Bombora, G2 Buyer Intent, or 6sense, Komo can ingest those feeds as a trigger layer, turning a data subscription into an end-to-end workflow rather than another alert waiting in a dashboard for manual follow-up.