What is intent data?

Intent data is behavioral evidence that a company or person is researching a problem, a category or a specific product, such as visits to your pricing page, comparisons on review sites, topic research across publisher networks, or hiring for a role your product supports. Where firmographics tell you who fits, intent data suggests when they might be ready to talk.

Intent data vs intent signals

The terms are often used interchangeably. An intent signal is a single data point (a pricing page visit, a G2 comparison, a relevant job posting). Intent data is the collected set of those signals across sources. In outbound, you act on signals and you buy, track or build intent data.

TL;DR

First-party: behavior on your own properties (website, product, emails, events). The most reliable and usually the cheapest.
Second-party: another company’s first-party data shared with you, such as review-site buyer intent or event attendee data.
Third-party: aggregated research behavior across publisher networks, plus public triggers like hiring, funding and tech changes. Broad, noisier, usually at account level.
Use it to time and prioritize, not to replace fit. Intent from a bad-fit account is still a bad-fit account.

Why it matters for a B2B pipeline

At any moment only a small share of a market is actively buying. Research from the LinkedIn B2B Institute and Professor John Dawes of the Ehrenberg-Bass Institute popularized the “95-5 rule”: roughly 5% of B2B buyers are in market in a given quarter. Intent data is how outbound finds more of that 5% instead of mailing everyone at random. It also helps with the other 95%: knowing which accounts are warming up tells you where to invest in ads and content before they are ready. Read more in If you only see the 5% in-market buyers.

Example

A company selling outbound infrastructure tracks three signals for ICP tier 1 and 2 accounts:

First-party: an identified company visited the pricing or deliverability pages twice in a week.
Public trigger: the account posted two or more SDR job openings in the last 30 days.
Third-party: an intent provider shows a topic surge on “cold email deliverability”.

Accounts with two of three signals go to a personal, signal-aware sequence written around SDR ramp-up (“adding SDRs usually means adding sending capacity, which is where deliverability breaks”). Accounts with one signal join a standard sequence. Accounts with none stay in ads and content only. The message references the situation, never the surveillance: “saw you visited our pricing page” puts people off.

How to use intent data

Start with first-party: website visitor identification, product usage, reply content and form answers (zero-party data).
Add public triggers: hiring, funding, leadership changes (job change signals), tech stack changes.
Add third-party topic intent (6sense, Demandbase, Bombora, G2 buyer intent and similar) once the basics work, and test it against outcomes before scaling spend.
Combine with fit in ICP scoring and lead scoring; require both before high-touch outreach.
Decay signals: most lose value within weeks, so refresh often and time-stamp everything.
Measure by tier: meeting and opportunity rates for signal-based outreach vs fit-only outreach.

Common mistakes

Treating third-party topic surges as hand-raises.
Creepy personalization that names the tracking.
Using intent to override fit.
Buying an intent platform before tracking your own website and replies.
Never checking whether signal-based outreach actually converts better.

For a practical playbook, read Signal before scale.

Intent data