Buyer Intent
Signals suggesting an account is actively researching a purchase, drawn from on-site behaviour or third-party data.
First-party and third-party
First-party intent is behaviour on your own properties — reliable, attributable, and limited to accounts that already found you. Third-party intent infers research activity across other sites, which reaches further and is considerably noisier.
Using it without overreacting
Intent data indicates a category is being researched, not that a purchase is imminent or that you are being considered. Treated as prioritisation it is useful; treated as a buying signal it produces poorly timed outreach.
The attribution trap
How to evaluate intent data before buying it
Establish where the signal comes from — your own site, a publisher network, review sites or bidstream — because provenance determines reliability.
Run a historical test: take accounts that showed intent six months ago and check whether they bought at a higher rate than matched accounts that did not.
Check the base rate. A provider flagging a third of your addressable market as in-market is not narrowing anything.
Decide what action a signal triggers before subscribing. Intent data with no attached play produces a list nobody works.
Measure the lift against your existing targeting rather than in isolation — the question is whether it beats what you already do.
Where it misleads
Research is not intent. Analysts, students, competitors and consultants generate identical signals to buyers, and the data cannot separate them.
Account-level attribution of anonymous activity is probabilistic and frequently wrong, and the error is invisible downstream.
Third-party intent tends to surface accounts already late in their process, by which point the shortlist is frequently formed.
Intent data is sold to your competitors simultaneously, so a signal frequently means everyone is about to call the same account.
RELATED TERMS
Account Scoring
Scoring at the account level rather than the individual, reflecting that B2B purchases are made by groups.
Dark Funnel
The buying activity that happens where analytics cannot observe it — private communities, peer conversations, group chats, word of mouth.
Ideal Customer Profile (ICP)
The definition of the accounts your product serves best, grounded in the observed characteristics of customers who buy quickly, stay, and expand.
Lead Scoring
A model assigning a numeric value to leads based on fit and behaviour, so attention goes to the ones most likely to convert.
Marketing Qualified Account (MQA)
The account-level equivalent of an MQL: an account showing enough aggregate engagement to warrant sales attention.
Revenue Signal
An observable data point that reliably precedes a revenue outcome — engagement patterns, stakeholder additions, usage changes.
COMMON QUESTIONS
- What is buyer intent data?
- Signals suggesting an account is actively researching a purchase, drawn from on-site behaviour, third-party publisher networks, review site activity or content consumption patterns. The claim is that it identifies accounts in market before they contact you.
- Is third-party intent data reliable?
- It varies enormously by provenance and should be tested rather than trusted. Run a historical check: take accounts flagged six months ago and see whether they bought at a higher rate than matched accounts that were not. Many providers do not survive that test.
- Why does intent data disappoint?
- Three reasons. Research is not intent — analysts, competitors and consultants look identical to buyers. Account attribution of anonymous activity is probabilistic and often wrong. And the same signal is sold to your competitors, so acting on it frequently means joining a queue.
- How should intent signals be used?
- As a prioritisation input alongside fit, never alone, and always with a defined play attached. A signal with no action produces a list nobody works. The most defensible use is first-party intent — behaviour on your own properties — where provenance is known and attribution is reliable.
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