Harnessing Intent Data: What It Really Means for B2B Tech Marketing

I joined Mike Maynard on the Marketing B2B Technology podcast to talk about something that’s shaping how modern B2B go-to-market actually works: intent data - what it is, why it matters, and how to use it in a way that genuinely moves pipeline rather than just adding noise.

This post expands on that conversation, turning the core ideas into practical frameworks you can use in your own strategy.

The shift in buyer behaviour

One of the key themes we discussed was how B2B buyer behaviour has shifted in the last decade - and especially in the last few years.

Today, most buying committees do the vast majority of their research before they ever interact with a vendor. By the time a prospect fills out a form on your site:

• They’ve often already shortlisted potential vendors.
• They’ve researched independently across search, social and sources you don’t own.
• The decision criteria are already taking shape internally.

As I shared on the podcast: by the time buyers reach your website to convert, 70-80% of those companies have already shortlisted their options.

That means the old model of “capture leads and nurture them” is increasingly incomplete. You need better insight into when and why buyers start engaging with your category in the first place.

Why traditional metrics fall short

If marketing is still judging success mainly by:

• conversions
• form fills
• email opens
• clicks

…then you’re only measuring the visible part of a buyer’s journey.

In practice, much of the meaningful buyer behaviour happens in the dark:

• anonymous research across third-party platforms
• private social group discussions
• untracked content consumption
• early-stage comparisons without identifiable engagement

Intent data helps you bridge that gap - not by replacing your core metrics, but by adding a behavioural signal layer that shows which accounts are actually thinking about your category right now.

What intent data actually is

There’s a lot of vendor language wrapped around intent data, but at its simplest:

Intent data is a behavioural signal that attention is being given to a particular problem area or solution category.

It doesn’t tell you someone is absolutely ready to buy. Rather, it tells you that an account or a group of accounts is:

• researching topics related to your value proposition
• engaging with related content or signals across digital channels
• doing so in a way that indicates interest rather than random exposure

This information has value because it helps you prioritise not just the who but the when.

How to use intent data effectively

Start with clear questions

Using intent data without clear questions is like buying more gauges for a car without knowing the route.

Useful questions include:

• Which accounts are exhibiting intent that aligns with our ICP this quarter?
• Is the intent rising or fading week-over-week?
• Which stakeholders inside those accounts are showing behaviour signals?
• How does this tie to existing pipeline or sales engagement?

Intent data should help you answer operational questions - not just fill dashboards.

Align marketing and sales around the same signals

One of the biggest mistakes I see in ABM and intent strategies is treating intent data as a marketing metric only - something to judge campaign hits.

In reality, intent data becomes powerful when it unifies how marketing and sales prioritise accounts. If a salesperson can see the same signal that marketing sees, it changes how they:

• prioritise outreach
• tailor their conversation
• sequence touchpoints

That alignment - sales and marketing operating from the same behavioural cues - is where the real ROI comes from.

Intent data and campaign design

Data by itself doesn’t drive outcomes. Outcomes are driven when intent signals change behaviour.

Intent data can inform:

• Targeted advertising - focus spend on accounts with rising intent trends
• Sequence timing - accelerate outreach when intent crosses a threshold
• Message relevance - craft narratives that reflect what accounts are actually researching
• Sales prioritisation - feed intent signals into CRM so sales knows when to pick up the phone

Intent data shouldn’t be a vanity metric. Its value comes from how it shifts your team’s behaviour.

Common pitfalls to avoid

In the podcast, we talked through some common misconceptions around intent data - things that lead teams to waste time without meaningful progress.

Mistake #1 - treating all intent signals the same

Not all intent is equal. Some providers aggregate millions of signals that are weak proxies for real buying interest. The key is:

• going beyond raw volume
• thinking in patterns or clusters
• tying signals back to behaviour trends over time

Bad intent signals can mislead you just as easily as they can help.

Mistake #2 - not aligning intent with your ICP

Intent data becomes noise if you’re watching the wrong accounts. Filtering intent through your Ideal Customer Profile and firmographic logic makes it actionable.

The role of AI in intent workflows

AI doesn’t generate intent data. But it can:

• surface patterns you would otherwise miss
• predict which signals are most likely to convert to pipeline
• prioritise accounts in context with your historical data

The key is not to automate everything, but to collapse complexity into decisions that humans can act on.

Tools have become better at spotting patterns - but humans still need to decide:

• which signals matter
• how to weigh them
• how to change behaviour because of them

Intent data + AI wins when it changes how you make decisions next week, not when it populates yet another dashboard.

A simple experiment to get started

If you want to put theory into practice, try this:

1. Define a focused list of 50-150 target accounts
   • no more than you can reasonably influence this quarter.
2. Set up simple intent tracking
   • using your provider or a combination of first-party + third-party signals.
3. Map rising intent to actions
   • e.g., when intent score rises for an account, trigger outreach or targeted ads.
4. Synchronise sales and marketing reviews
   • weekly check-ins where both teams look at the same signals and decide next steps.
5. Measure pipeline impact, not just intent volume

At the end of a quarter you’ll know far more about what actually works in your context than any one vendor claim.

Final thought

Intent data isn’t a silver bullet, and it’s not a standalone strategy.

It’s a signal layer - one that helps you read the market earlier, align your teams around behaviour rather than assumptions, and make your campaigns more precise and timely.

When you treat it as a behavioural priority signal rather than a metric to report on, it becomes a real competitive advantage in today’s crowded B2B tech landscape.

For more detail, you can listen to the full episode on the Marketing B2B Technology podcast here.