Intent data can be a powerful signal for B2B marketers, but relying on a single source or treating research activity as buying intent often leads to noise instead of pipeline.
Intent-based marketing has become one of the hottest tools in B2B demand generation over the last several years. The premise is straightforward: identify companies actively researching a solution category and engage them before your competitors do.
On paper, it sounds like marketing magic.
In reality, many marketers are discovering that intent data doesn’t always translate into qualified pipeline. The problem isn’t that intent signals are useless. Far from it. The problem is how they’re interpreted and how much weight marketers place on them.
Intent signals often point to research activity, not necessarily purchase intent.
Someone downloading a whitepaper, clicking into a webinar, or reading about a technology category may simply be learning. They might be evaluating competitors, doing internal research, preparing a presentation, or just trying to stay current on industry trends. None of those behaviors automatically mean they are ready to buy.
That disconnect between interest and actual buying intent is where many marketing programs start to run into trouble.
Not All Intent Signals Are Equal
Another issue is that not all intent data is created equal.
Some vendors build their entire “intent” model around engagement within their own communities. For example, if someone browsing a webinar community clicks on a session related to a topic, that activity may be flagged as strong intent.
Others rely primarily on third-party intent providers that track content consumption patterns across publisher networks.
Those signals can absolutely be useful. But they only tell part of the story. Treating any single signal as definitive buying intent is risky, particularly when the underlying data may lack context about who the person is or why they were researching a topic.
In some cases, attribution itself can be messy. Aggregated signals tied to shared infrastructure such as IP addresses may map multiple organizations to the same activity. That can result in marketing teams targeting accounts that were never actually researching their category in the first place.
Timing can also be a challenge. By the time an intent signal surfaces inside a marketing platform, the research activity that triggered it may have occurred weeks earlier. If a buying cycle has already moved forward, outreach based on that signal may arrive too late to matter.
And because many companies subscribe to the same intent platforms, the same signals can show up across multiple vendors at once. That can lead to a wave of outreach hitting the same accounts simultaneously.
Marketing Still Requires Doing the Work
The reality is that intent data was never meant to be a silver bullet.
Like most things in marketing, the real value comes from combining multiple sources of information to create a clearer picture of buying activity.
The strongest programs tend to layer together several types of data, including:
- First-party behavioral signals from your own properties
- Third-party intent data providers
- Firmographic and technographic context
- Market intelligence and analyst insight
- Account-level patterns across multiple individuals
Rather than relying on one signal, successful teams build what is essentially a signal stack.
This is also where AI is starting to help. AI models can analyze patterns across multiple data sources, helping marketing teams identify account-level trends and prioritize organizations that show sustained engagement across several signals.
But even with AI, the foundation still matters.
Start With Your Own Data
One of the most valuable signals marketers often overlook is their own first-party data.
What content are people consuming on your website?
What events are they attending?
What topics are generating repeat engagement across your audience?
This kind of behavioral data tends to be more precise because it reflects direct interaction with your content and brand.
In many cases, it provides a stronger signal than generalized third-party activity alone. Treating first-party engagement as the foundation of your signal strategy can dramatically improve the accuracy of intent-driven programs.
How We’re Thinking About Intent at Techstrong
At Techstrong, we’ve been exploring how to assemble a more complete view of buying activity across our ecosystem.
Our approach begins with mining first-party behavioral data across the Techstrong family of sites. From there, we layer in third-party signals including Bombora and HubSpot engagement data.
We then incorporate additional intelligence derived from Futurum’s research and analyst insights. Finally, we enrich that dataset with review and buyer-intent data from G2 through the Futurum–G2 partnership.
The goal isn’t to rely on a single signal. It’s to combine multiple perspectives on buyer behavior to create a more reliable intent signal.
We believe this layered approach may provide some of the strongest intent data available.
But like anything in marketing, your mileage may vary.
Let’s Compare Notes
Intent marketing continues to evolve as marketers experiment with new data sources, analytics, and AI-driven insights.
We’d love to hear what you are seeing in your own programs. Are intent signals delivering real pipeline for your organization? What sources have proven most reliable?
If you’re interested in learning more about how we are developing Techstrong’s intent capabilities, you can find additional details here:
https://techstronggroup.com/techstrong-intent-signal/
Because intent data can be incredibly useful. But it works best when it’s treated as one signal among many, not as a shortcut to finding buyers.
