Intent data

What is intent data?

Intent data is behavioral data — content consumption, search activity, review-site visits, technographic changes, social engagement — that flags which accounts are actively researching your category or evaluating solutions. It comes in two flavors. Third-party intent data is aggregated by providers like Bombora, 6sense, ZoomInfo, and G2 from publisher networks and review sites; it tells you when accounts across your TAM are spiking on relevant topics. First-party intent data is captured directly from your own properties — website visits, pricing page views, demo requests, content downloads, product trials.

The category exists because most B2B buying happens before the rep ever talks to the prospect. By the time a buyer reaches out, Gartner's research says they've completed 67% of the buying process — read reviews, watched demos, surveyed peers, narrowed the shortlist. Intent data flips the timing problem. Instead of waiting for the prospect to reach out at week 8, the rep gets a signal at week 1 — when the prospect is just starting research — and engages while there's still time to influence the shortlist.

For an AE running outbound, intent data is the difference between dialing 100 cold accounts hoping someone is in-market and dialing 10 accounts you know are spiking on your category this week. The first motion converts at 1–2%. The second converts at 8–15% (6sense benchmark 2025).

Third-party vs first-party intent data

Third-party intent data tells you which accounts across your TAM are spiking on topics. It's bought from data providers who aggregate behavioral signals across millions of publisher and review pages. The strength: broad TAM coverage, including accounts that have never interacted with your brand. The weakness: noisy — a topic spike doesn't always mean the prospect is buying your specific product.

First-party intent data tells you which known contacts and accounts are engaging with your properties — your website, your content, your community. The strength: high-quality signal, since the prospect is engaging with you specifically. The weakness: limited to accounts that already know you exist.

Modern outbound combines both. Third-party intent surfaces accounts in your TAM that just started researching your category — they don't know you yet, but they're in-market. First-party intent identifies the moment those accounts visit your website or download a piece of content. Together, the signals stack into a high-confidence buying-readiness score.

How to use intent data in outbound

1. Filter intent by ICP fit first. An intent spike from a non-ICP account is noise. Only accounts that match your ICP and just spiked are worth prioritizing.

2. Look at the topic, not just the account. An account spiking on 'sales engagement platform' is in-market for your category. The same account spiking on 'HRIS pricing' is in-market for something else. Match the topic to your product, not just the account.

3. Time the outreach to the spike. Intent signals decay quickly — most are stale within 14 days. Outreach within 5 days of the spike converts at 2–3x the rate of outreach 14+ days later.

4. Reference the topic, not the data source. 'I noticed your team is digging into sales engagement platforms — wanted to share how we think about that' lands. 'Bombora data shows your account is spiking' is creepy.

5. Layer intent with other signals. Intent + recent funding + exec hire is a much stronger signal than intent alone. Compound signals predict close rate; single signals predict reply rate.

Intent data quality — what to watch

Intent data quality varies wildly by provider. The two things that matter: source breadth (how many publishers and review sites contribute the signal) and topic granularity (how specifically the topics map to your category). Broad sources with shallow topic mapping produce false positives. Narrow sources with deep topic mapping produce true signal but miss accounts.

Most teams find intent data is most useful as a prioritization layer, not a triggering layer on its own. Intent + ICP fit + a second corroborating signal (funding, exec move, prior engagement) produces a high-confidence list of 10–30 accounts per rep per week. Intent alone produces a noisy list of 200+ accounts that overwhelms the rep.

Common intent data mistakes

1. Acting on intent without ICP fit. A spike from a non-ICP account is a distraction. Filter intent by ICP first; then look at the signal.

2. Treating intent as a triggering layer instead of a prioritization layer. Intent alone produces too much noise. Intent + ICP + a second signal produces a focused weekly list the rep can actually work.

3. Waiting too long. Intent signals decay within 14 days. Sequences that fire 21+ days after a spike are functionally cold; the prospect already chose a vendor.

4. Mentioning the data source in outreach. 'Our data shows you're researching X' is creepy and loses trust. Reference the topic, frame it as observation, never expose the data plumbing.

5. Buying intent data with no plan to operationalize it. Intent data sitting in a dashboard nobody opens produces zero pipeline. The data has to flow into the sequencer and the rep's daily prioritization, not just into a marketing report.

How Gangly uses intent data

Gangly's Signal Detection pulls intent data — both third-party from partners like Bombora and 6sense, and first-party from website and content engagement — and combines it with funding events, exec moves, technographic changes, and CRM history to surface high-confidence buying signals on ICP-fit accounts. The rep sees 5–15 high-priority accounts per week instead of a noisy dashboard of 200+ spikes.

Outreach Writer references the topic the prospect is researching — never the data source — and frames the first touch around the observation. Workflow Sequencer fires sequences within 24–48 hours of the spike so the timing carries the signal. The intent data stops being a static dashboard and becomes a live trigger on the rep's daily prioritization.

See how Signal Detection works →

Frequently asked questions