Intent data is information that signals a prospect is actively researching a problem, solution, or product category relevant to what you sell. It indicates buying intent: that someone is in-market right now, not just theoretically a good fit for your product.
The core idea is that timing matters in sales. Reaching the right person at the wrong time produces few results. Intent data helps identify when a prospect is likely to be receptive to outreach because they're actively thinking about the problem you solve.
Types of intent data
First-party intent data comes from your own systems: website visits, content downloads, demo requests, email opens, or replies. These are the strongest signals because you have direct visibility into the behaviour and the prospect has already engaged with your brand.
Second-party intent data comes from partner networks, where signals are shared between companies whose audiences overlap. Less common, but can be highly relevant depending on the partnership.
Third-party intent data comes from external providers that aggregate browsing behaviour across thousands of websites. They track which companies are visiting content on specific topics and sell that data as intent signals. Bombora and G2 Buyer Intent are common examples. These signals are broader and noisier than first-party data but can surface in-market companies before they've ever visited your site.
Social intent signals are worth calling out separately. LinkedIn post engagement (likes, comments on relevant content), job postings for specific roles, and profile updates signal context that's often more actionable than anonymous browsing data.
How intent data is used in outreach
Intent data most commonly changes who you prioritise and when you reach out, not what you say. A prospect visiting your pricing page three times in a week gets added to an immediate outreach sequence. A company posting five SDR job listings might get flagged as worth targeting with your outreach platform.
The challenge with third-party intent data is noise. Signals can be stale or represent a single researcher rather than a buying committee. First-party and social signals tend to be more reliable and specific.
How toflow.ai uses intent signals
toflow.ai's Enrichment Agent pulls LinkedIn post activity, company job postings, and profile data to surface intent signals before outreach starts. Configurable AI sub-agents can monitor specific signals, like new SDR hires at target accounts, and automatically trigger enrolment in the right outreach sequence when a signal fires.