Intent data tries to show which companies are actively researching what you sell.
Key points
- HubSpot describes buyer intent data as information that shows how likely a prospect is to buy, based on their online behavior [1].
- First-party intent comes from your own properties, such as site visits and email replies; third-party intent is aggregated from activity elsewhere on the web [1][2].
- Much third-party intent data has historically relied on cookies and tracking, which browsers and privacy laws increasingly restrict [3].
- Buyers do much of their research alone; Gartner reports they spend only about 17% of buying time meeting potential suppliers [4].
- Intent is one kind of Buying Signal; it works best combined with fit from Firmographics and the Ideal Customer Profile (ICP).
- Processing intent data about individuals in the EU or UK needs a lawful basis under GDPR, often Legitimate Interest [5].
Types of intent data
First-party intent is behavior you observe directly: visits to your pricing page, repeat sessions, webinar attendance, replies to emails and product usage, as in a Product Qualified Lead (PQL). It is the most reliable because you know where it came from. Third-party intent is collected across other websites and publishers, typically by tracking which companies read content on certain topics and flagging unusual spikes. HubSpot notes that intent signals help sales prioritize accounts showing active interest [1]. Third-party data usually resolves activity to a company rather than a person, often by mapping network addresses to organizations, a practice rooted in web tracking techniques [2]. Treat it as a probability, not a fact.
Privacy and accuracy limits
Third-party intent depends on tracking people across sites, and that is getting harder. Some browsers now block third-party cookies by default, which MDN's documentation notes are often used as tracking cookies to follow users across sites [3]. Privacy law also applies: under GDPR, processing personal data for marketing needs a lawful basis, and the UK regulator's guidance on Legitimate Interest requires a balancing test between your interest and the person's rights [5]. Accuracy is a separate concern. Company-level matching is noisy, topic models are vendor-defined, and a spike in research may come from a student, a competitor or an employee. Validate intent data against outcomes before letting it drive Lead Scoring heavily.
Using intent in outreach
The practical value of intent data is prioritization. Since buyers complete much of their research independently, Gartner's finding that only about 17% of buying time is spent with suppliers suggests the seller who arrives early with relevant help has an advantage [4]. Use intent to decide which accounts on a Target Account List to contact this week, not to write creepy messages that reveal what someone browsed. A better approach is to reference the business problem the research implies, backed by public context such as a Trigger Event. Combine intent with fit so that high intent from a poor-fit company does not outrank a strong-fit account, and track Positive Reply Rate by signal to see which sources predict real conversations [1].
Related terms
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