Over the past few years, classic outbound marketing has become less and less effective, if not completely ineffective. Given the resources required to run these complex, multi-channel outreach systems, revenue leaders have taken notice. Enter the emergence of the intent signal era.
Intent signals are timing signals. As buyers expect to control their purchasing process and self-navigate further and further down the funnel until they’re ready to talk to sales, think of intent signals as indicators that the timing might be right for sales outreach. When you can time your sales motion correctly, you have a much better chance at getting in the mix to win a customer than you would otherwise. That’s the objective, but it’s not that easy.
TL;DR:
- Intent signals are timing indicators, not buying confirmation.
- Blend first- and third-party data.
- Filter for fit. Intent without firmographic and technographic fit is just activity.
- Score at the account level, with tight recency thresholds.
- The data is a commodity. The response discipline is the differentiator.
What are some examples of intent signals?
First-party & third-party intent data: First-party data tells you who’s engaged with you specifically, while third-party data tells you who’s shopping the category. First-party signals come from your own properties: who’s visiting your pricing page, downloading your content, or attending your webinars. Third-party signals come from data providers like Bombora or G2, aggregating research activity across the web. Neither is sufficient alone. The strongest programs blend both, using third-party data to find accounts before they’ve raised their hand and first-party data to confirm the interest is real.
Firmographic and technographic overlays: Intent without fit is just activity. A mid-market company binge-reading content on your topics means little if they’re too small to afford you, or run on a tech stack incompatible with your offerings. Layering firmographic data (size, industry, revenue) and technographic data (their existing tools) on top of intent signals filters out accounts that will never close, regardless of how interested they look.
Keyword-level vs. topic-level intent: Some tools let you track specific keywords; others group activity into broader topic clusters. Keyword tracking gives precision but can miss adjacent research. Topic-level tracking gives coverage but can dilute signal strength. The right choice depends on deal complexity: simpler, higher-volume motions can lean on topic clusters, while complex enterprise sales benefit from the specificity of keyword-level tracking.
The number one thing to know
Intent signals decay… and fast. A spike in research activity from 45 days ago is a cold trail; a spike from three days ago is a live conversation waiting to happen. CEOs and CMOs need to push their teams to define explicit recency thresholds for scoring, because sales will burn credibility chasing stale signals as if they were fresh ones.
Sales and marketing alignment on intent-triggered plays is critical. Intent data is only as good as the speed of the response behind it. If an account crosses a threshold and nothing happens for two weeks, you’ve wasted the signal. Leaders should insist on a documented process between marketing and sales defining exactly what happens, and how fast, once an account shows real, agreed upon intent.
Other considerations
The most important thing to remember is that these are signals and not a fully complete view of what a buyer is doing and thinking. They are imperfect and incomplete. As such, they require some contextualizing and discernment to know what’s likely to be actionable or not. They’re data points that, when considered in total, can help engaged and discerning salespeople act at the right time.
False positive management
Not every spike in activity means real interest. Competitors research your content. Analysts and students show up in the data. Job changers browse out of curiosity. Filtering these out requires ongoing tuning, and leadership should expect this to be a maintenance cost of running an intent program.
Account-level vs. contact-level intent
B2B deals close through committees, not individuals. Scoring a single contact’s activity in isolation misses the fact that three other stakeholders at the same account might be researching independently. Aggregating signals at the account level gives a far more accurate picture of where real buying momentum exists.
Dark funnel activity
A significant amount of buyer research happens where you can’t see it: private Slack communities, podcasts, peer recommendations, analyst calls. This is the dark funnel, and it’s often where the most serious buying conversations start. This highlights how important it is to remember that the nature of intent signals is incomplete information.
Where to start
Intent signals aren’t a crystal ball, and treating them like one is the fastest way to lose faith in them. What they offer is a narrow window of time in which your outreach is likely to land. Capturing that value depends less on the data provider you choose and more on the discipline around it: fit filters, recency thresholds, account-level scoring, and a documented handoff. Build that operating rhythm first. The signals are only worth what your response speed makes them worth.
