Buying Signals: What Customers Are Telling You Without Saying It
Buying signals are the behavioural, contextual, and conversational cues that indicate a prospect is moving toward a purchase decision. They range from the obvious, like a direct enquiry or a pricing page visit, to the subtle, like a shift in the questions someone asks or the content they consume. Reading them accurately is one of the most commercially valuable skills in marketing and sales, and most teams are worse at it than they think.
The challenge is not that buying signals are invisible. It is that they are easy to misread, easy to ignore when the pipeline looks full, and easy to over-index on when it does not. Getting this right requires a combination of behavioural data, genuine customer understanding, and the discipline to act on what you see rather than what you hoped to see.
Key Takeaways
- Buying signals exist across three layers: digital behaviour, verbal and written cues, and contextual triggers. Treating any one layer in isolation produces a distorted picture.
- Intent data tells you what someone is researching, not what they will decide. It is a directional input, not a closing mechanism.
- The strongest buying signals are often the ones your CRM is not capturing: tone shifts in conversation, questions about implementation, and unprompted internal advocacy.
- Most teams confuse engagement signals with buying signals. High email open rates and social likes are vanity metrics unless they correlate with downstream revenue.
- Buying signal frameworks only work if sales and marketing are looking at the same data and using the same definitions. Alignment on this is rarer than most organisations admit.
In This Article
- Why Most Teams Misread the Signals in Front of Them
- What Are the Different Types of Buying Signals?
- How Intent Data Fits Into the Picture
- The Signals Your CRM Is Not Capturing
- Why Engagement Signals Are Not the Same as Buying Signals
- How to Build a Buying Signal Framework That Actually Works
- The Role of Customer Delight in Generating Buying Signals
- When to Act on a Signal and When to Wait
Why Most Teams Misread the Signals in Front of Them
Early in my career, I sat in enough client review meetings to notice a pattern. Account teams would present engagement metrics, website traffic, and lead volume as evidence that the campaign was working. The client would nod. And then the quarter would close and the revenue number would not move. Nobody had been dishonest. They had simply been measuring the wrong things and calling them buying signals.
The problem is structural. Marketing teams tend to optimise for what they can measure easily, and the metrics that are easiest to measure, clicks, opens, form fills, tend to sit at the top of the funnel. They are real signals of something, but they are not necessarily signals of intent to buy. A prospect who downloads a white paper might be a researcher, a competitor, a student, or a journalist. Treating every content download as a buying signal is how pipelines get inflated and forecasts get embarrassing.
Buying signals become meaningful when they cluster. A single pricing page visit is weak. A pricing page visit, followed by a case study download, followed by a return visit to the product comparison page, followed by a LinkedIn search for your company name, that is a pattern. The signal is in the sequence, not the individual action.
What Are the Different Types of Buying Signals?
Buying signals fall into three broad categories. Understanding the distinction matters because each type requires a different response from your team.
Behavioural Signals
These are the actions a prospect takes that indicate movement through a consideration process. In a digital context, they include: visiting high-intent pages like pricing, case studies, or implementation guides; returning to your site multiple times in a short window; spending significant time on product-specific content; using your search functionality with specific queries; or engaging with bottom-of-funnel content like ROI calculators or comparison tools.
Behavioural signals are the most trackable, which is why they attract the most attention. Tools like Hotjar can show you where users are spending time on your pages and where they drop off, giving you a clearer picture of which content is genuinely pulling people toward a decision. The limitation is attribution: you can see what someone did on your site, but not the full context of why.
Verbal and Conversational Signals
These are the signals that surface in direct conversations, whether in sales calls, discovery sessions, email exchanges, or live chat. They include: questions about implementation timelines, integration with existing systems, or team onboarding; requests for references or case studies from specific industries; questions about contract terms, payment schedules, or exit clauses; and the shift from “how does this work” to “how would this work for us.”
That last one is worth dwelling on. When a prospect stops asking about your product in the abstract and starts asking about it in the context of their specific situation, that is one of the clearest buying signals you will encounter. It means they have mentally started the process of imagining themselves as a customer. Most sales teams recognise this instinctively. The problem is that this insight rarely makes it back to marketing in any structured way.
Contextual and Trigger-Based Signals
These are external events that create buying conditions, often independent of any direct interaction with your brand. A company hiring aggressively in a function you serve. A leadership change that typically precedes a strategic review. A funding announcement that unlocks budget. A regulatory change that creates urgency. A competitor going out of business or being acquired.
Contextual signals are the most underused category in most go-to-market strategies. They require more effort to surface, because they live outside your own data, but they are often the highest-quality signals available. A prospect who just hired a new CMO and is actively rebuilding their agency roster is a fundamentally different prospect from one who has been quiet for six months. Same company, very different buying conditions.
If you are building or refining your go-to-market approach, the broader Go-To-Market and Growth Strategy hub has a range of frameworks and thinking on how to structure commercial strategy around real customer behaviour rather than assumed intent.
How Intent Data Fits Into the Picture
Intent data has become a significant part of the B2B marketing toolkit over the last several years, and it deserves a clear-eyed assessment. At its best, intent data, which typically tracks what topics or keywords a company’s IP addresses are researching across the web, gives you an early warning system for accounts that are entering a buying cycle before they have contacted you. That is genuinely useful.
At its worst, it creates false confidence. Intent data tells you that someone at a company is researching a topic. It does not tell you who, why, how seriously, or whether they have any budget or authority. I have seen teams build entire outreach sequences around intent signals that turned out to be a junior analyst writing a report, not a decision-maker preparing to buy. The data was accurate. The interpretation was not.
The Vidyard Future Revenue Report highlights the pipeline pressure that go-to-market teams are operating under, and intent data is often positioned as the solution to that pressure. It can be part of the solution, but only if it is used as a prioritisation tool rather than a replacement for genuine qualification. Use it to decide where to focus attention, not to skip the work of understanding whether a prospect is actually ready to buy.
The Signals Your CRM Is Not Capturing
When I was running an agency, we had a client in financial services who had been with us for three years. Solid relationship, consistent spend, no obvious tension. Then in the space of about six weeks, the tone of our monthly calls changed. Questions became more detailed. They started asking about how we handled transitions and knowledge transfer. They requested documentation we had never been asked for before.
None of that showed up in our CRM. The account was green across every metric. But those were clear signals that someone was doing due diligence, either preparing to bring things in-house or evaluating other agencies. We did not act on them quickly enough, and we lost the account. The signals were there. We just were not looking for them in the right places.
This is a common failure mode. Organisations invest heavily in tracking digital behaviour and almost nothing in capturing the qualitative signals that surface in human conversation. Some of the most reliable buying signals are:
- A prospect who starts introducing you to more senior stakeholders without being asked
- Unsolicited positive references to your company in industry conversations
- A prospect who pushes back constructively on your proposal, rather than going quiet, because engagement means they are still in the process
- Requests for information that only matter if a decision is imminent, like legal review of contract terms or security questionnaires
- A prospect who asks what the onboarding process looks like
That last one is almost always a strong signal. Nobody asks about onboarding unless they are seriously considering becoming a customer. It is a mental rehearsal of the post-purchase experience, and it means the consideration phase is largely over.
Why Engagement Signals Are Not the Same as Buying Signals
This distinction matters more than most marketing teams acknowledge. Engagement signals, high open rates, strong social media interaction, event attendance, content downloads, indicate that your audience finds your content interesting or useful. That is worth something. But it is not the same as indicating intent to purchase.
I have judged the Effie Awards, which are specifically designed to evaluate marketing effectiveness rather than creative quality alone. One of the things that process reinforces is how rarely engagement metrics translate directly to business outcomes. Campaigns that generated enormous buzz and social engagement sometimes moved the commercial needle barely at all. Campaigns that looked unremarkable on engagement metrics sometimes drove significant revenue growth. The relationship between attention and purchase intent is not linear, and it is not automatic.
The question to ask about any engagement metric is: does this correlate with revenue? Not theoretically, but in your actual data, for your actual customer base. If high email open rates consistently precede purchases in your funnel, then open rates are a useful proxy signal. If they do not, they are vanity metrics dressed up as buying signals.
This connects to a broader point about go-to-market strategy. As BCG’s work on commercial transformation has consistently shown, the organisations that grow most effectively are the ones that build tight feedback loops between customer behaviour and commercial decision-making. That requires treating signal quality as a strategic priority, not just a data hygiene issue.
How to Build a Buying Signal Framework That Actually Works
A buying signal framework is only as good as the alignment behind it. The most common failure is building a scoring model in marketing without meaningful input from sales, and then wondering why the “qualified” leads that come through are not converting at the expected rate.
When I was scaling an agency from around 20 people to over 100, one of the most commercially important things we did was create a shared language between the people generating leads and the people closing them. Not a complex lead scoring system, just a clear, agreed definition of what a good signal looked like at each stage of the funnel, built from the patterns we had actually observed in won and lost deals.
The process for building this kind of framework is more straightforward than most organisations make it:
- Audit your closed-won deals. Look at the 20 or 30 most recent deals you closed and map the signals that preceded the close. What did the prospect do in the 30 to 60 days before they signed? What did they ask? What content did they consume? What events triggered the conversation?
- Audit your closed-lost deals. Do the same for deals you lost. Look for signals that were present but misread, or signals of disengagement that were ignored because the account looked promising on paper.
- Identify the patterns that are predictive. Not all signals are equal. Some will appear in both won and lost deals. Focus on the signals that appear disproportionately in won deals and are absent or different in lost ones.
- Build your signal taxonomy. Categorise signals by type (behavioural, conversational, contextual), by strength (weak, moderate, strong), and by stage (awareness, consideration, decision). This gives you a working model rather than a list.
- Create feedback loops. Build a process for sales to flag when the signals they are seeing on the ground diverge from what the model predicts. The model should update based on what is actually happening in the market.
Tools like Semrush’s breakdown of growth tools cover some of the technology stack options for tracking behavioural signals at scale. Technology is useful here, but it is downstream of the framework. Build the model first, then choose the tools that help you execute it.
The Role of Customer Delight in Generating Buying Signals
There is a version of this conversation that most articles skip, which is the relationship between the quality of your product or service and the quality of the buying signals you receive. Genuinely delighted customers generate buying signals on your behalf. They refer colleagues. They mention you unprompted in industry conversations. They respond positively to re-engagement. They expand their own contracts without needing to be sold to.
Marketing is often deployed as a blunt instrument to compensate for a product or service that is not generating those organic signals. I have seen this pattern play out across many client relationships over the years. The brief arrives framed as a demand generation problem, but the underlying issue is that customers are not staying, not expanding, and not referring. No amount of buying signal optimisation fixes that. You can get better at identifying and responding to signals, but if the underlying customer experience is not creating advocates, you are running on a treadmill.
The most powerful buying signal is a warm referral from an existing customer. It arrives pre-qualified, pre-trusted, and often pre-sold. Building the conditions for that kind of signal to exist is a product and service quality question as much as it is a marketing one.
The BCG research on evolving customer needs in financial services makes a related point: understanding what customers actually need at different life stages produces fundamentally better signal quality than optimising for transactional intent alone. The same principle applies across sectors. When you understand the customer’s situation deeply, you stop chasing signals and start recognising them.
When to Act on a Signal and When to Wait
One of the more counterintuitive lessons from managing large-scale campaigns across multiple industries is that premature response to a buying signal can be as damaging as missing it entirely. Responding to a weak signal with high-pressure outreach pushes prospects away. Treating every content download as a sales-ready lead trains your sales team to waste time and trains your prospects to disengage.
The question is not just “is this a buying signal” but “what kind of signal is it and what is the appropriate response.” A prospect visiting your pricing page for the first time warrants a different response from a prospect who has visited it three times in a week, engaged with your case studies, and just connected with your CEO on LinkedIn.
Timing matters too. Vidyard’s analysis of why go-to-market feels harder points to the reality that buyers are more self-directed than ever. They are further through their decision process before they engage with a vendor, which means the signals that precede first contact are more meaningful than they used to be. By the time someone reaches out, they have often already made a shortlist. The signals before that contact are what tell you whether you are on it.
The broader frameworks and commercial thinking behind this kind of approach sit within the Go-To-Market and Growth Strategy hub, which covers how to structure your market entry, demand creation, and customer acquisition thinking around what actually drives revenue rather than what looks good in a dashboard.
About the Author
Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.
