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There’s a moment I’ve witnessed inside dozens of B2B teams across my career.
Part of the series: B2B Intent Data and Buying Signals, the full guide.
Marketing has invested in an intent data platform — Bombora, G2 Buyer Intent, TechTarget Priority Engine, 6sense, Demandbase, take your pick. Dashboards are live. Signals are flowing. A weekly export goes to sales. Leadership is excited. Slides with heatmaps of “surging accounts” get presented in QBRs.
Six months later, the tool is under review. Pipeline impact is unclear. Sales reps say the data is noisy. Marketing says sales isn’t following up. And the vendor’s customer success team is desperately scheduling a “re-engagement call” to prove ROI before the renewal conversation.
This story is so common it’s practically a genre. It pretty much is B2B in 2026
And it’s not a technology problem.
The platforms themselves are genuinely sophisticated. The data, while imperfect, contains real signal. The problem is almost always the same: organisations have invested in signal collection without building the machinery for signal interpretation. They’ve wired up sensors without building the intelligence layer that knows what to do when the sensors fire. A critical missing piece of the infrastructure is the ability to pipe these signals back into the marketing channels which have significant data exhaust - like premium (in other words ‘done right’) programmatic, where the interpreted signal can drive bidding fidelity, and ultimately account progression.
Since 2011 I’ve worked inside B2B and programmatic advertising — building Demand Side Platforms (4 and counting), a Sell side platform, a publisher adserver, an ad exchange (3 and counting), dynamic creative solutions, a patent for contextual advertising - alongside operating the tech in 2 different services businesses meaning building audience strategies, architecting intent layers, running media activations across the full demand spectrum — and I’m convinced that signal interpretation is the most under-invested capability in modern B2B marketing. It’s also where the real competitive advantage now lives.
This post is my attempt to articulate why most signal strategies fail, and what the ones that actually work have in common.
The Data Abundance Problem
B2B companies have never had more behavioural data about their buyers. The modern revenue stack is typically instrumented with website analytics, marketing automation, CRM activity, intent data platforms, installed-tech intelligence (Bombora’s technographic layer, HG Insights, Slintel), G2 and Gartner Peer Insights review activity, hiring signal feeds, content engagement scoring, LinkedIn interaction data, community intelligence, and in some cases dark social monitoring tools like Sparktoro or Wynter.
That’s a genuinely impressive set of sensors. And yet, in most organisations, this data is not creating proportional insight.
The paradox is that more signal data often makes the problem worse, not better. When everything is a signal, nothing is a signal. Teams get flooded with activity data, can’t distinguish meaningful patterns from noise, and gradually lose confidence in the entire approach. The intent data platform becomes one more dashboard that people check less and less frequently, until it becomes background infrastructure that nobody really trusts and nobody really owns.
The issue isn’t the data. It’s the interpretive framework around it.
Why Signals Are Not Leads — And Why This Distinction Matters Enormously
For most of the past two decades, B2B demand generation was built on a remarkably simple and remarkably durable model: capture engagement, create a lead, pass to sales. The underlying logic was linear. A person who downloads a guide has shown intent. A person who attends a webinar is warmer still. A demo request is the warmest signal of all. MQL scores were essentially a formalised version of this intuition — a numerical proxy for purchase readiness built on engagement thresholds.
Intent data has disrupted this model conceptually, but most organisations are still operationally running the old playbook. The signal has replaced the lead as the input, but the output is exactly the same: a list of accounts, a sales sequence, an outreach motion. The machinery hasn’t changed.
This matters because signals are fundamentally different from leads. A lead is a direct expression of a person’s willingness to be contacted — they submitted a form, they said yes. A signal is an inference drawn from observed behaviour. It tells you something happened, but it doesn’t tell you what it means without additional interpretation. We got hooked on the crack of inference signals without really understanding what they meant.
A contact downloading a thought leadership guide on supply chain resilience is probably learning about the topic. A contact visiting your pricing page three times in a week after attending two product-focused webinars is signalling something categorically different. Yet most sales teams receive both as equivalent “engaged account” notifications and respond with the same outreach sequence.
The consequences of this conflation are predictable. Buyers in early discovery stages get pitched before they’re ready, disengage, and go dark. Your domain gets mentally tagged as “too salesy.” And because the buying process is long — Gartner’s research suggests enterprise B2B sales cycles now routinely extend to 12–18 months — burning that early relationship is costly in ways that rarely show up in attribution models.
LinkedIn’s B2B Institute has documented what they call “the 95-5 rule”: at any given time, roughly 95% of your total addressable market is not actively in a buying cycle. They are, however, forming preferences, building mental models of the category, and developing opinions about vendors — all of which will influence the buying decision when the moment eventually arrives. Engaging those accounts with aggressive sales outreach doesn’t just fail; it actively damages your position for the future moment when they do enter market. Engaging them with information which is genuinely useful and accretive to their journeys progression provides positive brand memory.
I’d argue that if you lack information which definitively says an account is actively IN-MARKET RIGHT NOW then the default should be to the latter types of content
This is the foundational mistake of most signal strategies. They treat every signal as evidence of near-term purchase intent, when most signals are evidence of something far earlier and more fragile: curiosity.
Below is the signal operating model I’d put around any intent data tool, step by step, so sales actually uses it.