What is predictive intent data & how to use it in B2B marketing?

B2B teams have already scaled the hurdle of gathering data—thanks to first- and third-party cookies. The challenge has now shifted to ‘knowing’ what to do with that data.

Here’s the usual scenario: One account downloads an eBook. Another spikes in website visits. A third opens every nurture email. All of these signals look like ‘engagement’—of some sort.

But which of these actions indicate real purchase intent, and which are just noise?

Without a clear answer, GTM teams fall into a cycle that feels all too familiar. Marketing pushes these “engaged” leads into the pipeline, only for sales to complain they’re unqualified. Campaigns bloat with the wrong accounts, and sales cycles stall out.

That’s why the conversation around intent data has become so critical in modern go-to-market strategy. It promises to reveal what your buyers are actually researching and when they’re in-market.

But before we explore how predictive intent takes this even further, let’s start at the foundation: what is buyer intent data, and why does it matter?

What is buyer intent data?

Buyer intent data is information that reveals when an account or individual is actively researching a product, solution, or problem in your category.

It lets revenue teams spot the ‘signals’ buyers leave when they start researching online. These might include reading a whitepaper, comparing vendors, searching for a solution on Google, engaging on social media, or interacting with a competitor’s content.

Think of this as an ‘early warning system.’ It doesn’t tell you exactly who on the buying committee will sign the contract, but it does reveal that an account is actively considering a purchase—and what topics, products, or problems are driving that research.

Types of intent data

Intent data typically falls into three main categories, each with unique advantages and limitations:

1. First-party intent data

This is the intent you collect directly from your own properties — like website visits, content downloads, webinar attendance, email interactions. It’s also highly accurate because it’s based on direct engagement with prospects.

However, it only captures people who already know about you. If a buyer is researching your competitors or exploring solutions elsewhere, you won’t see it here.

2. Second-party intent data

Second-party intent comes from partnerships with other platforms, publishers, or vendors.

For example, if you advertise on an industry publication and the publisher shares anonymized data on which accounts are consuming content on certain topics, that’s second-party intent.

It gives you more visibility than just your owned channels but is still limited by the scope of the publisher’s audience.

3. Third-party intent data

This is aggregated at scale by intent data providers who track activity across thousands forums, search engines, and content networks. It includes signals like keyword searches, content consumption, or competitive research happening across the broader web.

A major plus to this is it offers scale and visibility into accounts you might never see otherwise.

However, it’s ‘noisier’ and not every spike in activity translates into real buying interest. Without proper filtering, teams risk chasing false positives.

Related → Buyer Intent Explained: B2B Sales Signals That Convert

The problem with buyer intent data

On the surface, the idea behind intent data sounds great: “If you know who’s already researching your category, winning deals will be easier.”

While that’s true, the application is also complex. The first issue is:

• Volume vs. quality: Many providers highlight the sheer scale of their intent data, but not all signals are equal. A company clicking on a single blog post may be logged as “interested,” but that hardly indicates readiness to buy. Teams end up wasting valuable time on accounts that never convert.
• Fragmentation is another issue: Most companies collect intent signals across multiple tools. They have a marketing automation platform for first-party data, an ABM platform for third-party, and publishers for second-party. These signals sit in different systems, making it hard to get a single, comprehensive view of which potential customers are truly in-market.
• Timing is also difficult: Intent signals often tell you that an account is ‘active’, but not when they’re truly ready for a conversation. A prospect might be casually researching long before budget or urgency is in place. In fact, they may already be deep in a buying cycle by the time the signals are detected.
• Content is missing: For example, you might see a spike in keyword research, but without understanding how it ties into account fit or prior engagement, it’s hard to know whether the signal is meaningful or just background noise.

Related → Intent Data (on Its Own) Is Just Wishful Thinking

How predictive analytics solves the intent gap

The main problem with traditional B2B intent data is that it only produces signals—that’s all. It tells you something is happening at an account, but you don’t know whether that something is meaningful, relevant, or worth your team’s time.

A solution to this execution gap is predictive analytics.

What is predictive analytics?

Predictive analytics is a process that uses machine learning models and advanced algorithms to analyze historical and real-time data, identify patterns, and forecast what’s most likely to happen next.

In a B2B context, it means analyzing thousands of data points across accounts to determine which signals correlate most strongly with actual closed-won deals.

How predictive analytics works

• Data ingestion: It pulls signals from multiple data sources: first-party (your website, CRM history ), second-party (review platforms, partners), and third-party (publisher networks).
  • They also enrich this with demographic data (like buyer roles and seniority), firmographic attributes (such as industry, size, and revenue), and technographic insights (the tools and platforms the account uses). This ensures predictive signals are grounded in both account-level fit and the right decision-makers.
• Pattern recognition: Algorithms look at historical closed-won and closed-lost deals to identify patterns.
  • For example, they might find that accounts showing intent on “compliance automation” combined with CFO engagement convert at a 3x higher rate.
• Signal weighting and lead scoring: The system assigns scores or probabilities that reflect how likely an account is to convert.
  • For example, a pricing page visit may count more than a blog read. Engagement from a VP of Operations may outweigh clicks from an intern. 
• Forecasting and prioritization: The output is a list of ranked and prioritized sets of opportunities. These predictions allow GTM teams to focus on the accounts most likely to move forward.

Why this matters for B2B marketing

Modern B2B buying journeys are complex, involving entire committees across multiple functions.

Traditional intent data struggles here because it can’t separate signal from noise or connect disparate actions into a coherent story. Predictive analytics bridges this by showing marketers and sellers which signals matter most and when to act.

• For marketing: It improves lead generation quality by focusing spend on high-fit, high-intent accounts.
• For sales: It provides clarity as reps don’t have to sift through endless “intent lists” to figure out who to call. They get prioritized, high-confidence accounts that are truly in-market.
• For leadership: Because predictions are based on historical success patterns, they streamline revenue decision-making by tying signals to outcomes.

Related → Ultimate Guide to Predictive Analytics for Go-To-Market Success | Demandbase

Predictive analytics + intent data: The winning combination

Predictive analytics doesn’t solve the problem alone. And this is because predictive models are only as good as the data you feed them.

If all you’re working with are historical sales records, CRM entries, or past campaign performance, the models can highlight patterns but still struggle to recognize real-time buying context. For example, you can have data on which accounts looked good last quarter, but not know which accounts are heating up right now.

Now buyer intent data does the opposite, it captures those fresh signals that reveal an account’s current mindset. But remember, intent data is ‘noisy.’

However, when the two are combined, you get predictive intent data: a smarter, signal-driven system that shows who’s active and predicts who’s ready to buy.

What is predictive intent data?

Predictive intent data is information that uses patterns of a potential customer behavior to forecast which accounts are most likely to enter or advance in a buying cycle. In this case, it layers analytics and modelling on top of raw intent signals to assign a probability score of an account converting.

Predictive vs. basic intent data: What’s the difference?

The key difference from the typical intent data lies in its ‘forward-looking nature.’

• Basic intent data tells you that an account has shown interest by searching for certain topics, attending an event, or increasing website activity.
• Predictive intent data takes those signals, compares them to historical patterns of past customers, and identifies which accounts are following similar journeys. It shows you which accounts are statistically more likely to convert, when they might be ready, and even which stage of the buying journey they are in.

For example, let’s say you’re selling HR software. Predictive analytics alone might flag a fast-growing enterprise as a strong fit. While intent data alone might show a surge in “HR automation” searches at a different company.

But predictive intent data reveals that the enterprise flagged as a fit is also now spiking in HR automation intent signals across review sites. This combined ‘vote of confidence’ tells you it’s a great-fit account that’s actively in-market.

Related → ABM Predictive Analytics | Demandbase

How to use predictive intent data to find your next customer

Prioritize high-fit, in-market accounts

The most direct application of predictive intent data is in prioritizing accounts. It shifts the approach from the typical ‘cold lists’ B2B sales teams receive to surfacing ranked sets of accounts that both align with your ICP and are showing real-time buying signals.

This means your SDRs and AEs start every week with clarity, knowing which account they should focus on.

For example, if your predictive model identifies a mid-market healthcare company as a high-fit account and intent data shows a spike in “HIPAA compliance automation” searches, that account rises to the top of your outreach queue.

By working this way, you fill your pipeline with accounts that are more likely to convert and at a faster rate.

Related → Find the Right B2B Buyers with Accurate Intent Data

Time your outreach with precision

One of the biggest advantages of predictive intent data is that it tells you when to engage.

Let’s say your predictive model flags an account as a strong ICP fit, but intent data shows no current signals. Since you’re unsure, it’s best not to waste sales cycles yet. Instead, let marketing keep the account in a low-cost nurture program.

When predictive intent surfaces an activity spike (multiple personas visiting a pricing page, or a surge in searches around your product category), then you make your move. Outreach at this moment feels helpful rather than interruptive, and will more likely increase your response rates.

Related → How to Use Intent Data for B2B Sales and Marketing

Personalize messaging around real buyer needs

Taking a cue from timing your outreach, personalization also comes in handy. But it’s only as good as the data that informs it.

With predictive intent data, personalization extends beyond the usual “[first_name]” snippets and focuses more on curating experiences around what the account is actively thinking about.

For example, if intent signals show that a company is researching “multi-cloud security,” your outreach can lead with pain points around cloud risk. Now if the predictive modeling adds that CFO engagement is a key conversion driver, your messaging can focus on ROI and compliance costs.

The aim here is to show the buyer that you understand their current priorities and your solutions fits it perfectly.

Improve ABM campaign targeting

The good and also bad side of account-based marketing campaigns is it’s heavily dependent on ‘who’ you’re targeting. So if you run ads to the wrong audience —there’s no do over, and you waste budget.

Predictive intent data mitigates this, targeting only accounts that are both fit and showing intent. Now when you run ads again, instead of blasting 1,000 accounts with generic ads, you can run highly specific campaigns to 100 accounts researching—e.g., “data observability platforms.”

This results in higher CTRs, stronger engagement, and campaigns that contribute directly to the pipeline.

Build smarter content journeys

Moving away from sales, predictive intent data is also important in improving your content strategy. When you analyze the topics your in-market accounts are researching, marketing can design relevant content journeys that match buyer interests.

For example, if your target accounts are showing spikes in searches for “AI in compliance,” you can create a webinar or case study on that topic. When predictive modeling shows that multiple stakeholders are consuming compliance-related assets, the marketing team can escalate them into mid-funnel nurture campaigns.

Detect early churn risks in customer accounts

Asides from discovering net-new pipeline, predictive intent also helps with retention. It can notify customer success teams to intervene when existing customers begin researching competitors or adjacent categories.

For example, if a large enterprise client suddenly spikes in engagement with competitor webinars, your CS team can proactively schedule a business review, reinforce value delivered, or explore upsell opportunities.

Expand into adjacent markets

Predictive intent data can also reveal opportunities outside your current ideal customer profile (ICP). For example, you may discover that a traditionally low-priority vertical (say, manufacturing) is suddenly surging in searches around your category.

Predictive analytics can validate whether accounts in this vertical resemble your past wins, giving you confidence to test expansion strategies.

This allows you to explore new segments with data-driven evidence. It’s a safer, more strategic way to expand market share while minimizing wasted GTM spend.

Predictive intent in action: How leading companies win

Coalfire

Problem

When cybersecurity leader Coalfire looked to modernize its go-to-market strategy, it struggled with three major pain points:

• Pipeline efficiency: Their ABM campaigns generated activity but not necessarily qualified opportunities, leading to wasted efforts and higher costs.
• Lead quality: Sales reps consistently complained about accounts that appeared “active” but weren’t actually ready for engagement.
• Attribution complexity: Fragmented tools made it nearly impossible to prove ROI on their ABM campaigns, undermining confidence in marketing’s impact.

Solution

By deploying Demandbase, Coalfire shifted from static ABM to a predictive, intelligence-driven GTM strategy.

Here’s how it worked:

• Data integration: Demandbase integrated seamlessly with Coalfire’s Salesforce and Marketo instances, creating a unified system of record that could evaluate accounts in real time.
• Audience segmentation: Next, Demandbase layered its intent insights on the ICP, allowing Coalfire to identify which accounts were actively in-market. This allowed them to refocus their GTM resources exclusively on high-fit, high-intent accounts.

With all these set up, Coalfire was able to run tightly targeted campaigns aligned to predictive signals. Plus, it helped optimize spend and increase precision in messaging.

As one Coalfire executive put it:

“Demandbase gave us the clarity and prioritization we needed to truly execute our ABM strategy, ensuring every dollar spent counts.”

Read full case study →

Palo Alto Networks

Palo Alto Networks, a global cybersecurity leader, faced one of the toughest GTM challenges in enterprise sales: reaching and influencing the right decision makers inside complex buying committees.

Traditional ABM campaigns, often built around broad audience definitions or individual leads, weren’t cutting through. The result was wasted budget, generic engagement, and missed opportunities to accelerate pipeline.

Solution

To solve this, Palo Alto Networks implemented a buying groups approach within their ABM strategy, powered by Demandbase’s advanced orchestration and intent capabilities.

Here’s how Demandbase enabled it:

• Audience segmentation with orchestration: Palo Alto Networks used Demandbase orchestration to carefully define and target buying groups across third-party channels.
• Smarter advertising with AI insights: Within Demandbase’s dashboard, they prioritized segments and optimized ad bidding for buying groups most likely to convert.
• Lead enrichment with LeanData: They expanded and identified new buying group members to ensure complete coverage of decision-makers.
• Seamless sales activation with Outreach: Signals flowed directly into Palo Alto’s BDR workflows, enabling personalized, timely follow-ups that aligned perfectly with marketing campaigns.

SAP Concur

SAP Concur is a business travel, expense, and invoice management software. But like many enterprise organizations, they hit a wall: despite running ABM campaigns, they struggled to move accounts efficiently through the funnel.

Early-stage visitors stalled in “awareness,” lead quality was inconsistent, and conversion rates lagged.

Solution

SAP Concur implemented Demandbase’s journey stage framework to personalize customer experiences based on where visitors were in the buying cycle.

Here’s how it worked in practice:

• Segmentation by intent signals: SAP Concur used Demandbase to analyze first-party behaviors (form fills, search patterns, content consumption) and enrich them with third-party intent signals. This identified which accounts belonged to “awareness” vs. “demand gen” stages.
• Tailored web experiences: Awareness-stage visitors were guided to educational content—blogs, ungated guides, and industry explainers. Demand-stage visitors, by contrast, were shown gated assets like whitepapers, pricing guides, and eBooks to capture higher-value leads.
• Dynamic optimization: SAP Concur adjusted content paths to ensure potential buyers always received the right type of information at the right time.

Lindsay Hasz, Director of Insights and Optimization at SAP Concur had this to say:

Read full case study → 

Your predictive GTM starts with Demandbase

Demandbase brings every layer of buyer intelligence (fit, intent, and engagement) into one platform so your teams have a single, unified source of truth.

You see precisely which accounts matter, what they’re interested in, and when they’re primed to take action.

This clarity transforms the way revenue teams operate. Marketing directs spend toward accounts that will actually convert. Sales focuses energy on prospects with real buying intent. And leadership gains the visibility to track pipeline impact with confidence, from first signal to closed deal.

Matthew Miller, Global ABX Principal at Workday captured it perfectly:

“Using the Demandbase integration with LinkedIn, we can enhance our ad campaigns by targeting specific companies with precision and increasing the relevance of ads to the target audience. By focusing on high-value accounts, we increased our conversion rates” 

Read full case study →

And for you, it gets even better. You get:

• AI-powered prioritization: Demandbase uses machine learning to stack-rank accounts by actual buying readiness, helping your teams focus only on the ones that matter most.
• Full-funnel orchestration: Whether it’s programmatic advertising, sales outreach, or ABM campaigns, Demandbase pushes insights back into your workflows so every channel works in harmony toward revenue.
• Predictive pipeline acceleration: With Demandbase you can identify accounts earlier in their buying journey, allowing you to engage before competitors even know they’re in play.
• Deeper buyer journey insights: Demandbase tracks accounts across the entire funnel, showing you who’s active, and where they are in their journey—so you can match the right message to the right moment.

Predictive intent data only delivers value when it’s actionable and contextualized. That’s exactly what Demandbase enables: a GTM motion that is sharper, faster, and built on intelligence that drives revenue.