There’s no shortage of information about buyers anymore. A revenue team can see when someone visits a website, downloads a report, attends an event, watches a video, opens an email, returns to a pricing page, or starts researching a topic across the wider web. Account intelligence platforms can combine those activities.
Intent providers can identify changes in research patterns. Customer relationship management systems can connect them to sales activity. Then dashboards pull everything together and turn it into scores, stages, alerts, and neat little arrows pointing upwards.
From a distance, the logic seems reasonable. If you can see more of what buyers are doing, you should be able to make better decisions about them. Yet many revenue teams are still left with the same questions.
Which accounts are genuinely moving? Which buyers are still learning? Who needs help? Who wants to speak to sales? And which promising-looking opportunities are mostly a collection of clicks wearing a very convincing trench coat? The problem isn’t a lack of buyer data.
It’s that buyer activity has become easier to record at the same time that buyer behaviour has become harder to understand. More signals can improve visibility. They don’t automatically explain what’s happening behind them.
Revenue Teams Have Never Had More Buyer Data
The modern revenue stack can capture an extraordinary amount of activity. Marketing automation platforms record campaign engagement. Website analytics show which pages people visit and how long they stay. Intent data can indicate when an account starts researching a category more heavily than usual.
Revenue intelligence platforms connect those actions with emails, meetings, calls, opportunities, and account history. Artificial intelligence is adding another layer. It can identify patterns across large datasets, summarise account activity, rank opportunities, recommend follow-up actions, and flag behaviour that might otherwise be missed.
A decade ago, many teams were working with little more than form fills, email responses, and whatever sales representatives remembered to enter into the CRM. The difference is significant. But visibility creates its own expectations.
Once an organisation has invested in revenue intelligence, it expects the resulting information to make the path ahead clearer. If an account is researching several related topics, visiting high-value pages, and engaging with campaigns, the system should be able to tell the team what to do next.
Sometimes it can. Sometimes it produces a score of 92 and leaves everyone to decide what 92 actually means. That’s the buyer signal paradox. Revenue teams can see more activity than ever before, while remaining uncertain about what buyers are trying to accomplish.
The Original Promise Was Simple
Revenue technology developed around a practical problem. Marketing teams needed a way to identify which leads deserved attention. Sales teams needed to know where to spend their time. Leadership needed to understand whether campaigns were contributing to pipeline and revenue.
Early lead generation models relied heavily on direct responses. Someone submitted a form, requested information, or downloaded content. That action created a lead, and the lead moved into a process. As digital buying became more complex, the measurement models followed.
Lead scoring assigned values to different behaviours. Attribution tools tried to connect marketing activity with commercial outcomes. Account-based marketing shifted attention from individual contacts to organisations. Intent data looked beyond owned channels to identify broader research activity.
Revenue operations brought marketing, sales, data, systems, and process closer together. The assumption behind these developments was sensible. If teams could collect enough evidence, buyer behaviour would become increasingly measurable. And for a while, the biggest problem really was visibility.
Teams couldn’t respond to behaviour they couldn’t see. They couldn’t coordinate around account activity that lived in separate systems. They couldn’t improve a buyer journey they couldn’t follow. Technology helped close some of those gaps. But buying behaviour didn’t remain still while the tools improved.
Buyers gained access to more information, more channels, more communities, more independent research, and now more AI assistance. The environment revenue technology was designed to measure has changed around it.
Instead, Buying Behaviour Is Becoming Harder To Understand
A buyer signal is an observable action that may indicate interest, research, evaluation, or some other form of engagement. The important word there is “may”. A person visiting a pricing page could be building a shortlist.
They could also be checking a figure for a report, researching a competitor, comparing renewal costs, or trying to understand whether the product is completely outside their budget.
A spike in account-level research might indicate an active buying process. It could also come from one employee preparing a presentation, several people attending the same conference, or an existing customer investigating an adjacent service.
The action is real. The meaning remains open. This has always been true to some degree, but the number of possible explanations is growing. Modern buyers move between vendor websites, search engines, social platforms, communities, podcasts, reviews, analyst research, private conversations, and AI tools.
Each environment reveals a different fragment of the journey. The result is an expanding collection of buyer signals that can be individually accurate without creating a complete picture when combined.
This is where revenue teams can run into trouble. Data from several platforms may look like confirmation because it all points towards activity. But five systems detecting activity don’t necessarily provide five independent pieces of commercial evidence. They may simply be recording different parts of the same research moment.
More account engagement doesn’t always mean more buying progress. Sometimes it means the buyer has more questions.
AI Is Changing How Buyers Research
Generative AI is changing the mechanics of B2B research. A buyer can ask an AI tool to explain a technical category, summarise a report, compare suppliers, identify common implementation risks, analyse a request for proposal, or help draft an internal business case.
Much of that work once required several searches, multiple website visits, downloaded documents, spreadsheets, meetings, and a heroic number of browser tabs. Now it can happen inside one conversation.
Gartner reported that 45 per cent of 646 surveyed B2B buyers had used AI during a recent purchase. The same research found that 67 per cent preferred a buying experience without a sales representative, although buyers still valued seller input when they needed context or validation.
Forrester’s research points to an even broader range of uses. Buyers reported using AI for product research, supplier comparisons, analysing RFP responses, and building business cases. In other words, AI isn’t only helping buyers find information. It’s becoming part of how they organise and apply it.
That changes the relationship between research activity and visible engagement. A buyer may learn about several providers through an AI-generated answer without clicking through to every website. They may compare product capabilities without downloading a comparison guide.
They may use information gathered from reviews, analyst commentary, social posts, and vendor content without leaving a clear trail back to each source. TrustRadius found that 72 per cent of surveyed technology buyers had encountered Google AI Overviews during research. It also reported that 38 per cent referred to AI tools somewhere in the technology buying journey.
The traditional search path was relatively easy to recognise. A person entered a query, clicked a result, visited a page, and possibly completed another action. AI-assisted buyer research can remove several of those visible steps.
That means two things can happen at once. The buyer can move through a large amount of information more quickly, while the organisations being considered receive fewer direct clues about how that research is developing.
The Signal Environment Has Expanded Beyond Traditional Channels
Buyers don’t experience a company through one controlled journey. They move between information environments depending on what they need.
A vendor website may help them understand the product. A review platform may show how customers experience it. An analyst may explain the wider market. A podcast may reveal whether the company’s experts understand the problem beyond their own offering.
Then there are industry communities, private messages, professional networks, peer conversations, newsletters, social media, events, and internal documents shared between colleagues. AI increasingly sits across all of them, pulling information together before presenting it back to the buyer in a condensed form.
Forrester describes this shift as the collapse of the traditional search-to-click path. Buyers still gather information from websites, reviews, industry associations, and social platforms, but AI-powered interfaces can now summarise and filter those sources before the buyer visits them directly.
This doesn’t mean first-party data has become useless. Website behaviour, campaign engagement, and sales activity still provide valuable evidence. They’re simply no longer a reliable map of the entire B2B buying journey.
A person who has visited the website five times isn’t automatically more informed than someone who has never appeared in the analytics. The second buyer may have listened to two podcasts, read independent reviews, spoken to a colleague, and asked an AI tool to compare the company with three competitors.
One journey creates a trail the vendor can see. The other may be commercially significant but mostly invisible. Revenue teams therefore need to be careful about treating direct visibility as a measure of total influence.
More Signals Can Create More Uncertainty
It’s easy to assume that uncertainty decreases as information increases. In practice, large volumes of information can make interpretation harder, especially when different systems use different definitions.
An intent provider may flag an account as active. Marketing automation may show limited first-party engagement. The CRM may contain no current opportunity. A sales representative may know that the account renewed with a competitor six months ago. An AI scoring tool may still rank it as a priority.
None of those systems has necessarily failed. They’re answering different questions with different data. Intent data may show unusual research. Marketing automation records known interactions. The CRM reflects the sales process. The representative has relationship context. The AI model finds patterns based on the information it receives.
Problems begin when all of those outputs are treated as versions of the same answer. An account can show strong research activity without having an approved project. It can engage with content while reviewing the market for a future planning cycle.
It can involve several stakeholders who disagree about the problem, the solution, or whether anything should be purchased at all. Forrester found that 73 per cent of B2B purchases involved at least three departments, with an average of 13 people inside the buying organisation and another nine external participants influencing the decision.
At that scale, a rising engagement score may represent growing agreement. It may also represent growing disagreement. The data alone can’t tell you which one you’re looking at.
Visibility and understanding are not the same thing
Visibility tells a revenue team what happened. Understanding helps it judge what the event means within a real commercial decision. That distinction sounds simple, but it changes how buyer data should be used.
- A webinar registration tells you that someone registered. It doesn’t tell you whether they’re researching for themselves, supporting a colleague, following an industry trend, or evaluating a purchase.
- An account surge tells you that research activity has increased. It doesn’t tell you whether the organisation has budget, agreement, urgency, or a clear reason to change.
- A reply to a sales email tells you that someone is willing to talk. It doesn’t tell you whether they’re ready to buy.
Revenue teams have spent years improving visibility because visibility was limited. The next capability may be learning how to combine data with commercial context, buyer needs, account history, and human judgement. Not every signal needs a larger score. Some need a better question.
What This Means For Revenue Leaders
The shift from collecting buyer signals to understanding them changes more than reporting. It changes how revenue teams decide where to focus, when to engage, and what they expect their systems to tell them.
The immediate temptation is to treat this as a data problem. Add another source. Improve the dashboard. Refine the score. Build a more sophisticated model. Sometimes that helps. But the deeper issue is usually the decision being made from the data.
A revenue team can have excellent information and still ask the wrong question of it. An account score might show that activity is increasing, for example. But leadership still needs to decide whether that increase justifies a forecast change, a sales response, a campaign adjustment, or no action at all.
That judgement falls differently across each revenue function.
For CROs
For a CRO, the main concern is whether the organisation is building a pipeline it understands or simply one it can count. Those aren’t the same thing. A large pipeline can create reassurance because it suggests the business has options.
But volume becomes less comforting when opportunities have been created from signals the organisation can’t explain. If an account entered the pipeline because activity passed a threshold, leadership needs to know whether anyone has since confirmed the commercial conditions behind it.
That means looking beyond whether an account is active and asking whether the opportunity has a credible reason to move.
- Is the problem important enough to compete with other priorities?
- Is the organisation trying to change something now, or gathering information for later?
- Has the decision gained support beyond the first interested contact?
- Does the account understand the cost of doing nothing?
These questions won’t all be answered by marketing or technology. They emerge through a combination of research, sales conversations, account knowledge, and evidence gathered over time. This affects forecasting too. Forecast confidence shouldn’t rise simply because more activity has been attached to an opportunity.
It should rise when the business has stronger evidence that the buyer’s decision is developing. For CROs, then, better interpretation means creating a clearer standard for what counts as commercial progress. Without one, pipeline quality becomes vulnerable to whatever the revenue stack happens to measure most easily.
For VP sales
Sales leaders often feel the pressure created by buyer signals more directly. An alert appears. An account is researching. A contact has returned to the website. Someone has opened three emails in a week. The obvious response is to move quickly before a competitor does. There are times when that instinct is right.
But speed without context can turn useful information into poor sales engagement. A representative reaches out because the system says an account is active, yet the message has no connection to what the buyer is trying to understand. The account sees another generic follow-up. Sales sees another contact who “wasn’t ready”.
The missed opportunity sits between those two experiences. Better interpretation gives sales teams a reason for the conversation, not just a reason to make contact.
That may mean understanding which topic is receiving attention, whether the account’s behaviour has changed over time, what relationship already exists, and which part of the problem appears to be creating interest. It may also mean deciding that direct outreach isn’t the best next step.
Sometimes the more useful response is a relevant report, an analyst discussion, a podcast episode, or a piece of evidence that helps the buyer answer the question they’re already asking. This isn’t about making sales slower or passive. It’s about using context to improve the quality of the first move.
For VP Sales, the practical shift is from asking, “How quickly did we follow up?” to asking, “Did our response make sense for what we knew?” That creates a more useful standard for sales effectiveness, especially as buyers move between independent research and direct engagement more freely.
For revenue operations
Revenue operations sits in the middle of the problem because it helps define how behaviour becomes action. This team decides which systems connect, how data moves between them, what gets scored, when an alert is created, and which information reaches sales. Small choices inside those processes can shape thousands of commercial decisions.
That gives Revenue Operations a responsibility that goes beyond maintaining clean data. It needs to make the logic behind the system visible. If an account is prioritised, teams should be able to understand why. If several signals contributed to the decision, they should know whether those signals came from different sources or repeated the same underlying behaviour.
If a score changes, they should be able to see what caused the movement. Otherwise, the organisation ends up with a number that looks precise but can’t be questioned properly. This is where signal quality becomes more useful than signal volume.
Quality doesn’t necessarily mean a signal is stronger. It means the signal is clear enough to support a particular decision. A pricing-page visit may be useful when deciding what content to send next. It may be much less useful when deciding whether an opportunity belongs in the forecast.
Revenue Operations can help by separating signals according to purpose. Some indicate attention. Some suggest a change in research behaviour. Some help identify the likely topic of interest. Others become more meaningful only when combined with sales feedback or account history.
This creates several useful questions:
- Which activities influence account scores?
- Do all roles receive the same weighting?
- Are repeated actions treated differently from isolated ones?
- Can the model distinguish sustained research from a brief spike?
- Does sales feedback change how signals are interpreted?
- Are scoring rules tested against eventual outcomes?
- Can teams see why an account has been prioritised?
Not every action needs to feed one universal score. In many organisations, that may be the real next step. Moving away from systems that compress all behaviour into a single number and towards models that preserve more of the context behind it. That makes the data easier to challenge, improve, and use responsibly.
For demand generation leaders
Demand generation leaders face a slightly different risk. When campaign performance is judged mainly through visible engagement, teams naturally create more of the activity the system rewards. More clicks. More registrations. More downloads. More names entering nurture streams.
Those outcomes can show that a campaign attracted attention. They can’t always show whether it helped the buyer make sense of the decision ahead. That distinction changes what useful demand generation looks like.
A strong campaign shouldn’t only give someone a reason to engage. It should help them understand the problem more clearly than they did before. It might give them language they can use internally, evidence they can share with another stakeholder, or a way to compare approaches without reducing the decision to a product checklist.
This is where content quality becomes part of buyer-signal quality. If a campaign gives people something useful, the activity it generates carries more context. The revenue team knows what question the content answered, what issue it helped frame, and what kind of buyer would find it relevant.
A registration for a broad promotional webinar says very little on its own. A registration for a focused discussion about a specific operational challenge tells the team more about what may be driving the interest. The difference comes from the thinking behind the campaign.
For demand generation leaders, visibility should therefore support a better understanding of the buyer, not become the final measure of success. The most valuable programmes may not be the ones that create the highest number of responses. They may be the ones that create the clearest link between what buyers are trying to understand and what the organisation can help them decide.
Taken together, these shifts point towards a broader change in revenue leadership. CROs need stronger evidence behind pipeline. Sales leaders need context behind outreach. Revenue Operations needs transparent logic behind prioritisation. Demand generation leaders need campaigns that make buyer activity more meaningful.
None of those teams can solve the problem alone. But when they agree on what different signals can and can’t prove, buyer data becomes far more useful. It stops acting like a verdict and starts becoming what it should’ve been all along: evidence that helps people make a better commercial decision.
Final Thoughts: Visibility Doesn’t Guarantee Understanding
Revenue teams wanted a clearer view of the buying journey, so they built systems capable of recording more of it. That investment wasn’t wasted. Modern buyer data can reveal changes, patterns, and opportunities that would’ve been missed entirely a decade ago. But the environment has moved again.
Research now happens across more channels. AI is condensing information and removing familiar steps from the discovery process. Buying groups are involving more people, each with their own questions and sources. The signals reaching revenue teams are growing in number while representing a smaller share of everything shaping the decision.
So the next commercial advantage may not come from seeing more activity. It may come from understanding the limits of what can be seen. The organisations best positioned for this shift won’t be the ones that abandon data or rely on instinct instead.
They’ll be the ones that combine account intelligence with context, judgement, and a clearer view of what genuine movement looks like for their buyers. For years, revenue technology has helped organisations ask, “What did the buyer do?” The more useful question now may be, “What does that action tell us about the decision they’re trying to make?”
That’s where buyer data starts becoming commercially useful. It’s also where the conversation around sales readiness becomes much more interesting. At EM360Tech, we spend a lot of time examining the factors that shape enterprise buying decisions before they appear in a pipeline report.
Because understanding buyer behaviour isn't about collecting more signals. It's about recognising which signals help explain what buyers are actually trying to achieve.
