Buyer intent data is the trail of clues people leave while researching a purchase: pages viewed, guides downloaded, repeat visits, time on site, and topics they read about across other websites. Sales and marketing teams use it to spot who’s interested and decide who to contact first. It still has value, but it’s losing ground for two reasons. More research now happens inside AI assistants, where those clues aren’t collected. And intent data only ever describes what someone did in the past, when the decision is often being made right now.
How intent data has worked
For years the model was simple. First-party intent data comes from your own website and emails. Third-party intent data comes from providers such as Bombora, G2 and 6sense, which track research activity across networks of publisher and review sites and tell you which companies are reading about your category. Teams score that activity, and a high enough score triggers a sales call or an automated email sequence.
It works best for spotting trends: a spike in interest in a topic, a cluster of companies in one sector researching the same problem, a market warming up before a campaign. That’s still useful for forecasting and planning.
The limits of intent data
Signals are retrospective. By the time they’ve been collected, scored and acted on, the moment that produced them has often passed.
A visitor who downloaded your pricing guide yesterday may have a specific objection today. Someone who browsed eight pages could be seriously interested or simply lost. Intent data shows interest, but it doesn’t reliably show urgency, hesitation or the question that’s stopping someone from getting in touch.
Then there’s the shift in how buyers research. A growing share of B2B buyers now ask ChatGPT, Gemini, Copilot or Perplexity to compare suppliers, explain options and shortlist vendors. Those conversations don’t happen on the publisher sites that intent providers monitor, so a buyer can be deep into a decision before any signal reaches you.
Is intent data still useful when buyers research with AI?
Yes, for strategy and planning. It still shows which topics and sectors are warming up, and first-party data still tells you which of your pages matter. What it can’t do is help with the buyer who’s on your site right now, mid-decision, with a question.
That’s where AI moves from predicting behaviour to taking part in it.
From predicting behaviour to responding to it
Traditional systems track behaviour, raise a score and trigger a follow-up later. An AI assistant on your website responds while the behaviour is still happening.
Say a finance director is comparing your pricing tiers and hesitates over one feature. A well-built AI chatbot can explain the difference immediately. If security becomes the priority halfway through the conversation, it can switch to your certifications and data handling without sending her back to a menu.
The conversation itself becomes the signal, and a richer one than page views:
- a question rephrased twice suggests uncertainty;
- a move from features to compliance shows a new priority;
- a return to pricing after reading about integrations may mean the decision is close.
Instead of storing these signals for next week’s report, the system can respond in the moment, and pass a summary to your sales team when a person should take over.
Intent data tells you what a buyer did yesterday. A good AI conversation tells you what they need now, while there’s still time to help.
Speed builds trust
When clarification is delayed, doubt creeps in. When a buyer gets a clear, accurate answer straight away, confidence grows. A system that doesn’t make people wait, repeat themselves or click through endless menus earns trust quietly.
AI doesn’t replace human judgement here. It moves effort to where it counts. Routine questions get the same accurate answer every time, at any hour. The conversations that need nuance and discretion, such as a complex deal or a sensitive objection, go to a person who isn’t worn out from answering the same question five times that day.
The advantage comes from combining the two: historical signals for planning, real-time conversation for the buyer in front of you.
How we approach this
We design and build AI assistants that answer from your own content, connect to your CRM and hand over to your team with the full context, using AI-assisted development with our in-house engineers. If a website chatbot is where you’d start, our AI chatbot page explains how we set them up.
FAQs
What is buyer intent data?
Buyer intent data is information about a person’s or company’s research activity that suggests they may be planning a purchase, such as pages viewed, content downloaded and topics read across other websites. It’s used to prioritize sales and marketing follow-up.
Is intent data still useful?
Yes, especially for spotting trends, planning campaigns and understanding which topics and sectors are active. It’s less reliable for timing individual conversations, because it describes past behaviour and misses research that now happens inside AI assistants.
What can replace intent data for real-time engagement?
A conversational AI assistant on your website can respond to what a buyer asks in the moment, adapt as their priorities shift and pass a summary to your sales team. The questions people ask are often a clearer signal of intent than the pages they view.
