It begins with understanding who to target, when to engage, and why it matters.
Most B2B organizations don't have a shortage of buying signals. They have a shortage of context, prioritization, and coordinated action.
We have more information about our prospective customers than ever before. We can see who's visiting our websites, researching competitors, engaging with content, hiring new executives, expanding into new markets, or investing in new technologies.
And yet, one of the most common questions I hear from revenue leaders is surprisingly simple: How do we know which of these signals actually matter and which are just noise?
It's a fair question.
Having access to more buyer intelligence doesn't necessarily make your go-to-market strategy more intelligent. In fact, it can create more noise for marketing teams to analyze and more accounts for sales teams to pursue.
While the instinct may be to collect every possible signal, the goal should be to understand:
- Which changes in a prospective customer's business create a need for your solution
- When those changes occur
So that you can build a coordinated and personalized response.
That's where AI-assisted research and a connected CRM like HubSpot can make a meaningful difference.
First, let's rethink what constitutes a buying signal
When most people hear buyer intent, they think about observable engagement like someone visiting a pricing page, downloading a resource, researching a G2 category, or interacting with an advertisement.
Those behaviors can be valuable. But they're only one part of the picture.
Some of the most meaningful indicators of potential demand have nothing to do with your company's marketing activity.
Consider a staffing company that notices a prospective customer rapidly expanding into new markets and posting dozens of open positions. That growth may indicate an immediate need for recruiting support, temporary staffing, or specialized talent that the company's internal hiring team can't accommodate.
Or consider a manufacturer whose prospective customer announces plans to open a new production facility. That expansion may create demand for equipment, raw materials, components, or additional supply chain partners to support increased production capacity.
Neither prospective customer has necessarily visited a vendor's website or demonstrated traditional purchase intent.
But something has changed in their business that may create a need for a product or service. And those changes are often observable long before a buyer starts actively evaluating vendors.
That's the distinction I encourage revenue teams to make.
Instead of starting with "Who's interested in us?" start with "What's happening in our customers' businesses that makes what we do more valuable?"
This shifts signal research from monitoring engagement to understanding the conditions that create demand and investigating fit earlier in the buyer’s journey.
Segments tell us who. Signals tell us when. Context tells us why.
Another question I hear frequently is whether traditional segmentation still matters in a signal-driven GTM strategy.
Absolutely. In fact, I'd argue that segmentation becomes even more important.
Signals will never replace your ideal customer profile, but they are critical to helping you prioritize and engage the right accounts at the right moments.
I think about this in three dimensions:
- Segments tell us who. Which companies are a strong fit based on industry, size, business model, technology environment, geography, or other meaningful characteristics?
- Signals tell us when. What changes, activities, or events suggest that a business need may be emerging?
- Context tells us why. What does that change mean for this particular account, and how does it connect to the problems we solve?
Imagine you're a commercial staffing company specializing in skilled manufacturing and industrial labor. Your ideal customers are midmarket manufacturers operating multiple facilities in regions where qualified labor is difficult to find.
That's your segment.
Now imagine one of those manufacturers announces plans to open a new production facility and begins posting dozens of job openings for machine operators, maintenance technicians, and production supervisors.
That's your signal.
But the context is what makes it interesting.
The company is preparing to increase production capacity, potentially on an aggressive timeline. It may need to hire specialized workers faster than its internal recruiting team can support, particularly if it's entering a new labor market.
That's the context.
Together, those three dimensions create a much more compelling reason to engage than a generic outbound campaign targeting manufacturing companies.
And importantly, they help determine what to say.
Instead of reaching out with a broad message about staffing services, you might offer insights into local labor availability, compensation benchmarks, or strategies for building a workforce ahead of a facility opening.
The difference is relevance.
Context also helps determine what to do next.
Imagine that the same manufacturer is already a customer, with unfilled positions at an existing facility. A generic prospecting email about its expansion would miss the relationship entirely. The account owner may need to address those staffing gaps before discussing support for the new location.
The signal hasn't changed. The context changes who should respond, what they should offer, and what the conversation should include.
The TLDR; Segmentation helps you identify the right customers. Signals help you recognize the right moment. Context helps you understand how to make the conversation valuable.
Not every signal is a good signal
Imagine an HR compliance solution using third-party intent data to identify companies researching FMLA, the Family and Medical Leave Act.
On paper, it makes perfect sense. The company sells HR compliance solutions, and FMLA compliance is directly relevant to the problems its customers need to solve. If a company is researching FMLA, shouldn't that indicate potential interest in its solution?
Not necessarily.
When you think about it a bit harder, the people researching FMLA are often employees trying to understand their own leave eligibility or benefits. They aren't necessarily HR leaders evaluating compliance solutions.
The topic is relevant to the industry, but the behavior doesn't reliably indicate a buying need.
That's an important distinction: Just because someone is researching a problem your product solves doesn't mean they're in the market for your solution.
It's also why I recommend evaluating potential buying signals against five criteria:
1. Relevance: Does this signal indicate a business problem that your solution addresses?
FMLA research was certainly relevant to HR compliance, but it wasn't necessarily relevant to a purchasing decision. There's a difference between interest in a topic and evidence of an organizational need.
A more meaningful signal might be a company expanding into multiple states, creating additional complexity around employment regulations and leave administration.
2. Reliability: Can you trust the underlying information and what it actually represents?
An intent provider might accurately report increased research activity around FMLA. But that doesn't tell you who was researching it, why they were researching it, or whether the activity represented an organizational buying initiative.
The data itself might be accurate while the interpretation is wrong.
3. Timing: Does the event create a plausible window for engagement?
Someone researching FMLA because they're preparing to take leave has an immediate information need, but that doesn't create a timely opportunity to sell a compliance solution to their employer.
On the other hand, a company announcing a major workforce expansion across several states may be approaching a point where its HR compliance processes need to evolve.
Both signals involve relevant topics. Only one suggests a plausible business buying trigger.
4. Actionability: Can your team do something useful with the information?
A useful signal should inform a meaningful next step.
If a company is expanding into new states, an HR compliance provider might offer guidance on navigating different leave requirements, evaluating policy consistency, or preparing HR operations for a larger workforce.
That's a much more relevant conversation than reaching out simply because someone at the company researched FMLA.
5. Evidence: Do you have reason to believe this signal is associated with better commercial outcomes?
This is where historical opportunity analysis and experimentation become important.
It's easy to build a convincing narrative around why a signal should indicate buying intent. It's another thing entirely to demonstrate that accounts exhibiting that signal are more likely to engage, enter an opportunity, or become customers.
Before investing heavily in a signal, look for evidence that it actually helps predict the outcomes you care about.
These criteria help distinguish interesting information from useful intelligence.
And often, the real value emerges when multiple signals appear together.
For example, a company expanding into several new states, hiring additional HR operations staff, and researching leave administration may represent a much stronger opportunity than a company exhibiting FMLA research activity alone.
The combination provides more context about what's happening inside the business and why a solution might be needed.
The goal isn't to assign a magical buying-intent score to every company. It's to build a more informed hypothesis about where your team can create value. Sometimes, the most valuable insight is recognizing that a signal you've been paying to track isn't telling you what you thought it was.
Do you need one signal provider or several?
This is usually where the technology conversation begins.
Which sales intelligence platform has the best signal data? Should we use HubSpot's native capabilities? Do we need ZoomInfo, 6sense, Bombora, Clay, or something else?
My answer is that it depends on the signals you're trying to detect.
Different providers offer different types of intelligence.
Some specialize in company and contact information. Others focus on account-level research intent, organizational changes, technographics, or first-party behavioral data.
There's nothing inherently wrong with using multiple providers. In fact, complementary sources can help create a more complete picture.
But I would be cautious about assuming that adding another platform automatically improves your strategy.
Multiple providers may have overlapping datasets. They may report the same underlying event differently. They may disagree about which accounts are showing intent. And not every signal will be equally accurate, current, or relevant to your business.
Before purchasing another intelligence platform, I'd ask:
What specific business change are we trying to detect that we can't reliably identify today?
If you can't answer that question, you're probably not ready to evaluate vendors.
Start with the signal strategy, then determine which sources are necessary to support it.
You may find that your existing HubSpot capabilities, CRM history, and a targeted enrichment source are sufficient to run a meaningful pilot.
Turning signal research into action in HubSpot
Finding a meaningful signal is only the beginning.
The real opportunity is connecting that intelligence to the customer context already living in your CRM and using it to coordinate a relevant response.
This is where I see organizations getting stuck.
They invest in intent data, create dashboards, and give sales teams lists of accounts that are supposedly showing interest.
But what happens next?
Who owns the account? Is there already an open opportunity? Is the company a good fit? Has someone from sales recently reached out? Does the signal indicate a reason to start a conversation, or should marketing continue nurturing the relationship?
Without those answers, even high-quality signal data can become another disconnected source of information.
HubSpot can help operationalize this through a combination of CRM intelligence, segmentation, scoring, workflows, and sales execution capabilities.
I think about signal activation in four stages.
1. Research: Identify the conditions that create demand
Start with your ICP and the business problems you solve.
Use AI to research the events, initiatives, and organizational changes that might create those problems. Identify which signals are observable and where that information could come from.
2. Validate: Separate meaningful signals from noise
Evaluate each signal for relevance, reliability, timing, and actionability.
Look for combinations that provide stronger context. Assess whether the signal is detectable at scale, whether your historical data supports the combination, and whether the available data is trustworthy enough to support action.
3. Activate: Connect signals to coordinated GTM plays
Bring the relevant signal data into HubSpot and associate it with the appropriate company or contact records.
Use your existing segmentation to determine which accounts qualify. Combine signal information with CRM context, such as account ownership, lifecycle stage, engagement history, and active opportunities.
Then define the appropriate response.
That requires making your team's operating knowledge explicit. Document what qualifies an account for sales follow-up, what evidence supports a deal moving stages, and when a signal calls for education rather than a sales conversation. Include the problems your company solves, the services it can deliver, and the proof points that support its recommendations.
These playbooks give people and AI a consistent basis for choosing a response. Context informs why an action makes sense and what it should contain, while automation provides the means to carry it out.
Depending on the use case and your HubSpot configuration, that might involve dynamic segments, lead scoring, workflows, account-owner notifications, sales tasks, targeted campaigns, or AI-assisted prospecting.
Not every signal should automatically trigger outbound outreach.
Sometimes the best next action is additional research. Sometimes it's a relevant marketing campaign. Sometimes it's a conversation with an existing customer about a potential expansion opportunity.
The signal should inform the decision, not dictate the action.
4. Learn: Measure what actually works
Finally, close the loop.
Which signals lead to qualified conversations? Which combinations are associated with opportunity creation? Which campaigns produce meaningful engagement? Are signal-driven accounts converting at higher rates than comparable accounts?
And just as importantly, which signals consistently produce false positives?
This feedback should inform your research priorities, provider investments, scoring criteria, and GTM plays.
Over time, your signal strategy becomes less dependent on assumptions and more grounded in observed outcomes.
What this looks like in practice
Let's use a hypothetical cybersecurity company as an example.
The company specializes in helping growing businesses manage security controls, compliance requirements, and infrastructure risk.
Through AI-assisted research, the team identifies a potential buying trigger: companies expanding into new regulated markets may need additional cybersecurity and compliance support.
Rather than targeting every company announcing expansion, the team identifies several observable signals:
- A public announcement about entering a new geographic market.
- Hiring activity for compliance or security leadership.
- Evidence of increased investment in cloud infrastructure.
Individually, these signals may not tell us much but together, they suggest a business initiative worth investigating.
HubSpot contains the context to determine whether the company fits its ICP, whether there's an existing relationship, who owns the account, and whether anyone is already pursuing an opportunity.
If the account meets the defined criteria, a workflow could create a research task for the account owner, accompanied by a summary of the relevant signals and their sources.
Rather than sending a generic cybersecurity pitch, the seller might initiate a conversation about security and compliance considerations when expanding into regulated markets.
Marketing could support the same audience with an expansion-readiness assessment or targeted educational campaign.
The team would then track whether accounts exhibiting this signal combination progress to qualified conversations and opportunities at higher rates than comparable accounts.
That's the difference between collecting signal data and building a signal-driven GTM motion.
AI makes signal research easier. It doesn't eliminate the need for judgment.
One of the most exciting things about AI is how accessible sophisticated market research has become.
You don't necessarily need a dedicated research team to explore which organizational changes might create demand for your solution.
You can use AI to analyze your offering, examine your ICP, identify possible triggers, research relevant data sources, and generate hypotheses about how to engage.
A practical starting point is to work on what your team already repeats. If sellers repeatedly ask AI to prepare discovery questions, capture the instructions, account context, and standards needed for useful results in a reusable skill. Connected tools and scheduled tasks can help put those instructions to work. The value comes from applying a useful approach consistently across accounts.
The caveat is that while AI, for example, can make a compelling case for why a company hiring a new executive might need your solution. That doesn't mean the company has a budget, an active initiative, or any intention of buying. It can also suggest highly specific signals that turn out to be difficult to detect reliably.
That's why human judgment, source verification, and outcome measurement remain essential. Just because you’re using intelligence doesn’t mean you need to automate every step of prospect research and outreach. It's to make better decisions about where to focus your team's attention and how to engage more meaningfully.
Start with one signal, not an entire intelligence ecosystem
If you're looking to build a signal-driven GTM strategy, I wouldn't start by buying additional platforms or trying to operationalize dozens of signals at once.
Start with one customer segment and one business problem you solve particularly well.
Identify a small number of observable changes that might create that problem. Research how to detect them, evaluate their reliability, and determine what a relevant response would look like.
Then build a pilot in HubSpot.
Make sure the right accounts are identified, the right people are notified, and the resulting activity can be measured.
Learn from what happens before expanding the program.
You may discover that the signal you expected to be most valuable produces very little engagement. You may find that two signals are significantly more useful together than individually. Or you may learn that the biggest opportunity isn't acquiring new customers at all, but identifying expansion opportunities within your existing customer base.
That's the point of experimentation.
The future of signal-based GTM isn't about collecting more intent data. It's about understanding which business changes create demand, recognizing when those changes occur, and building a revenue engine that knows how to respond.
Your next best customer may already be telling you something.
The question is whether your organization knows what to listen for, what it means, and what to do next.
