Everyone’s Talking About Intent. Few Know How to Use It Right
Every B2B marketer has heard the same advice: use intent data to find prospects who are ready to buy. Yet for most businesses, the promise of B2B intent data has not matched the reality.
Too many tools claim to identify high-intent buyers, but in practice the results are often unclear and inconsistent. Between algorithmic predictions, overlapping data sources, and over-automated outreach, it is easy to mistake noise for genuine opportunity.
The truth is that intent-based marketing only works when the data is understood, verified, and applied with purpose.
This article breaks down what B2B intent data actually is, how to collect and interpret first-party intent data, and how to use AI lead scoring and human judgment together for effective lead qualification.
By the end, you will know how to turn intent signals into a practical B2B marketing strategy that drives reliable lead generation and real pipeline growth.
What Exactly Is B2B Intent Data?
B2B intent data is behavioural information that shows when a company is likely to buy, based on digital engagement signals. These digital signals include website visits, keyword searches, content downloads, and repeated engagement that indicate active interest in a solution.
In simple terms, intent data helps marketers and sales teams understand who is researching, what they care about, and how close they are to making a decision. This matters because intent data bridges the gap between brand awareness and purchase intent.
According to research by the Ehrenberg-Bass Institute, up to 95 per cent of businesses are not in the market for most products at any given time, with only 20 per cent in the market over a year and just 5 per cent in a quarter.
Tracking these early buyer intent signals means you can identify prospects before they raise their hand. Unlike traditional lead scoring, which starts after a form submission, intent data captures the silent research phase, such as when a company begins searching for “CRM integrations” long before contacting a vendor.
Where Intent Data Is Collected: First-Party vs Third-Party Signals
B2B intent data can come from two main sources, known as first-party and third-party signals.
First-party intent data refers to behavioural information collected from your own marketing channels, such as website visits, email engagement, gated content downloads, or CRM interactions.
This data is highly accurate because it shows how prospects are engaging directly with your brand. However, smaller companies often face a challenge with limited traffic and volume, which can restrict insight.
Third-party intent data, on the other hand, is aggregated from external publishers, ad networks, or intent data providers such as Bombora and Demandbase. These sources offer greater scale but can vary in accuracy, freshness, and relevance to your specific audience.
There is also second-party intent data, which comes from partnerships or co-marketing agreements where data is ethically shared between businesses.
Each type has its role in a balanced B2B marketing strategy that supports AI lead scoring, lead qualification, and targeted B2B lead generation. The challenge is not collecting data, but understanding which intent signals genuinely indicate purchase readiness.
Making Sense of Intent Data (Without Getting Misled)
Interpreting B2B intent data accurately is essential if you want to avoid wasting time, money and effort on leads that will never convert. Not every signal carries the same weight. Strong intent signals include repeated visits to high-value pages such as pricing or product comparisons, while weak signals might be a single blog view or a quick site visit.
The key is to evaluate recency, frequency, and context rather than relying on volume alone. A recent burst of activity across multiple relevant pages is a far stronger indicator than a large number of scattered interactions over months. Third-party “intent spikes” can often be misleading, as they may reflect research activity or content syndication rather than genuine buying interest. An example could be an analyst reading about a topic rather than a buyer researching vendors.
Always cross-check external data against your own first-party intent data and feedback from your sales team to confirm whether the behaviour fits a real opportunity. For instance, a company showing high intent could simply be analysing competitors.
The most effective B2B marketing strategy blends AI lead scoring with human review to validate buyer readiness. Ultimately, intent data is valuable only when interpreted through context and human judgment, not when treated as an automated shortcut to lead qualification or B2B lead generation success.
How SMEs Can Turn Intent Data Into Real Pipeline
For small and mid-sized businesses, using B2B intent data effectively does not require expensive platforms or complex integrations. What matters is setting up a simple, repeatable process that turns basic intent signals into qualified opportunities.
Step 1 – Collect Lightweight Signals
Start with free or affordable tools such as GA4, HubSpot CRM, email analytics, or the LinkedIn Insights Tag. Track behaviour patterns like repeated visits to key pages, returning company domains, and resource downloads. These insights form the foundation of your first-party intent data.
Step 2 – Verify and Score
Blend behavioural data with manual research, such as checking a company’s recent LinkedIn activity or CRM notes. Assign a simple 1-to-3 intent score based on how recently and deeply they engaged with your brand.
Step 3 – Engage Thoughtfully
Use intent data to guide the timing of your outreach, not to trigger automated sequences. For example, reach out personally after a visitor downloads a guide and views your pricing page, referencing what they interacted with.
Step 4 – Refine
Review which signals actually correlate with replies, meetings, or conversions. Build short feedback loops between marketing and sales to improve lead qualification and future campaigns.
Case Study – Bynder’s Intent-Led Outbound Framework
Bynder used layered intent data from 6sense, G2, and its own website analytics to prioritise outreach manually. BDRs received daily reports highlighting accounts moving into decision stages and used them to prioritise personal outreach (not automated). Within four months, Bynder increased its outbound pipeline by 2.5 times.
The lesson for SMEs: success comes from timing, layering and recognising patterns you can act on manually, not from chasing more data.
How Larger Firms Can Turn Data Volume into Predictive Intelligence
For larger organisations, the real value of B2B intent data lies in turning high data volume into predictive intelligence that can guide strategy and sales focus. Scaling intent effectively is not about more automation, but about cleaner integration, smarter scoring, and tighter feedback loops.
Step 1 – Integrate and Unify
Start by merging data from CRM systems, marketing automation, ABM platforms, and advertising analytics into one source of truth. Establish clear governance so everyone in marketing and sales shares a consistent definition of what “intent” actually means.
Step 2 – Score and Model
Use AI lead scoring and predictive analytics to identify multi-contact buying patterns. Weight signals according to historical conversion rates, giving stronger emphasis to actions that have previously led to successful lead qualification and deals.
Step 3 – Align and Activate
Share insights with sales in a way that adds context, such as topics of interest or recent engagement, rather than sending automated email sequences. The goal is to help sales teams reach out at the right time with informed, human conversations.
Step 4 – Measure and Feedback
Feed sales outcomes back into your AI models to improve accuracy over time. This creates a learning cycle that shifts your approach from reactive monitoring to predictive forecasting.
Case Study – Kibo’s Intent-Led Outbound Success
Enterprise software company Kibo used intent data from G2 to improve the accuracy of its outbound targeting. Before this, the team’s SDRs were spending significant time and effort on accounts that were not actively researching solutions.
By layering Buyer Intent signals with their existing CRM and demand generation systems, they were able to prioritise accounts already engaging with relevant product categories and competitor pages. Instead of blasting generic outreach, SDRs used this data to time their contact with accounts showing genuine purchase activity.
The results were significant. Kibo reported that while their average outbound conversion rate was around 4.8%, the accounts flagged through intent data converted at roughly 14%, almost three times higher. The company also saw improvements in lead qualification, team efficiency, and overall B2B lead generation performance.
The key takeaway is that at enterprise scale, success with intent data depends on precision, timing, and human interpretation, not automation alone.
Using AI Responsibly: Prioritise Signals, Keep Humans in the Loop
Using AI on B2B intent data requires caution. AI is powerful at spotting patterns and ranking signals, but it should always operate under clear rules and human oversight.
For SMEs, the best use of AI is in lightweight support tasks such as surfacing accounts that show repeated engagement inside your CRM or email platform. Always review that shortlist manually before reaching out to ensure accuracy and relevance.
For larger enterprises, AI lead scoring should remain tightly connected to first-party intent data. Train predictive models only on closed-won outcomes that reflect genuine buyer behaviour, not vanity clicks or raw traffic.
The strength of AI lies in its ability to prioritise signals, not to make decisions for you. Remember that AI is only as reliable as the data it learns from. If the third-party or first-party intent data it uses is incomplete or inaccurate, those errors will be amplified, not corrected.
The most effective B2B marketing strategy combines AI intent scoring with human insight to ensure precision and context. In short, AI should improve timing and focus, but personal judgment should always guide the final decision in lead qualification and B2B lead generation.
Conclusion: Intent Data Isn’t a Shortcut, It’s an Amplifier
B2B intent data is not a shortcut to growth, but an amplifier of a strong foundation. It works best when strategy, sales alignment, and clear processes are already in place.
For SMEs, the priority should be clarity and consistency in how first-party intent data is collected and interpreted.
For larger enterprises, the focus should be on data accuracy, integration, and continuous feedback between marketing and sales teams.
Above all, intent data helps you listen better to your market, not shout louder at it. When used with purpose, it sharpens your targeting, improves lead qualification, and supports smarter B2B lead generation.
If you want to turn your intent data into real opportunities rather than noise, book a funnel and copy review with Alpha P Tech and discover where better insight can unlock more predictable growth.
