How Can You Build a Converting Lead Scoring Model Using Intent Data?

77% of B2B buyers won’t talk to a salesperson until they’ve done their own research.

That means buyers are out there searching, reading, and comparing long before they engage with sales. So, how do you know when they’re actively looking for your solution?

To figure this out, you need real-time intent data—data that tells you:

✔ Who is searching
✔ What they are searching for
✔ When they are searching for it

A strong lead scoring model helps you rank prospects based on their intent so you can identify the best-fit leads and guide them towards conversion.

But to do this effectively, your model must be updated and rely on real-time data—not just static information like job titles and company size.

So, what’s wrong with traditional lead scoring?

Why Traditional Lead Scoring Doesn’t Work Anymore

Traditional lead scoring doesn’t track what buyers are doing at the moment. It:

• Doesn’t show real-time intent—just because someone downloads a whitepaper doesn’t mean they’re ready to buy.
• Focuses on fit, not action—a lead that matches your ICP is useless if they aren’t searching for a solution.
• Ignores competitor research—if a lead is comparing options on G2 or Capterra, that’s a major buying signal.

According to a MarketingSherpa study, 79% of B2B marketers are not engaging in lead scoring.

This suggests that a large portion of businesses may be missing out on the benefits that lead scoring can offer.

Yes, companies using intent-based lead scoring are already ahead. Instead of tracking who the lead is, they track what the lead is doing right now.

But what exactly is intent-based lead scoring, and how does it work?

What is Intent-Based Lead Scoring?

Intent-based lead scoring focuses on real-time buyer behavior instead of just demographic or firmographic details. It tracks:

✔ Pricing page visits – A strong sign they’re considering a purchase.
✔ Competitor research – Checking out reviews and comparisons on sites like G2, TrustRadius.
✔ Repeated product page visits – They’re digging deeper into features.

When you track real-time behavior, businesses can prioritize the leads most likely to convert.

How Do You Filter Out Useless Intent Signals?

Not every action means a lead is ready to buy. Someone who reads one blog post isn’t the same as someone checking your pricing page three times in a week.

To filter real intent, businesses must assign different scores to different actions.By giving more weight to high-intent actions, sales teams can focus on leads who are ready to buy now.

But how do you actually build an intent-based lead scoring model?

How to Build an Intent-Based Lead Scoring Model

Step 1: Identify Key Intent Signals

Most companies track too many signals, making lead scoring complicated and ineffective. The key is to focus on behaviors that truly show buying intent.

Repeated pricing page visits – They are seriously considering your solution.
Webinar attendance – They are investing time to learn about your product.
Demo requests – The clearest signal they are ready to talk.

What about low-intent actions?

Social media clicks – Doesn’t always indicate buying intent.
Blog reads – Good engagement, but not a sign they are ready to purchase.

Tracking too much data makes it harder to prioritize leads effectively.

So, how do you balance intent with traditional lead scoring?

Step 2: Combine Intent with Traditional Scoring

Intent data is powerful, but it doesn’t tell the full story. Some leads may be interested, but not the right fit for your business.

A strong scoring model should combine:

✔ ICP Fit – Are they the right size, industry, and job role?
✔ Behavior – Have they engaged with emails or downloaded content?
✔ Intent Signals – Are they actively researching and comparing solutions?

For example:

A VP from a large company (great ICP fit) but no intent signals → Lower priority.

A mid-level manager from a smaller company who visits your pricing page daily → High priority.

By tracking both fit and intent, sales teams focus on leads who are both qualified and ready to buy.

What next, what if your lead scoring model isn’t delivering results?

Step 3: Optimize and Improve Over Time

The biggest mistake companies make? Setting up a lead scoring model and never updating it.

Buyer behavior changes. What worked last year might not work today.

Regularly check your model by:

✔ Analyzing conversion rates – Are high-scoring leads actually becoming customers?
✔ Adjusting score weightage – Are some intent signals more valuable than others?
✔ Refining ICP criteria – Are you targeting the right industries and job roles?

Businesses that update their lead scoring quarterly see a 35% boost in conversion rates (McKinsey).

Now that you know how to build a better model, which type of lead scoring system should you use?

Which Lead Scoring Model is Right for You?

There are three main types of lead scoring models. Here’s how they compare:

1. Rule-Based Lead Scoring (Simple but Limited)

Assigns fixed points based on predefined rules (e.g., +10 for email opens). Easy to implement but lacks flexibility.

This model works best when combined with intent scoring.

2. Predictive Lead Scoring (AI-Powered & Highly Accurate)

Uses machine learning to analyze past conversion patterns and predict which leads will buy.

Companies using AI-driven scoring see a 40% boost in sales efficiency (Gartner).

3. Intent-Based Scoring (Best for Real-Time Accuracy)

Tracks real-time buyer behavior and prioritizes leads actively searching for a solution.

This model is most suitable for B2B companies targeting high-value accounts.

How Do You Convert Lead Scores into Conversion?

A great scoring model is useless if sales and marketing aren’t aligned on what to do with high-scoring leads.

Here’s how to make your lead scoring model work:

✔ Set clear thresholds – When does a lead move from MQL to SQL?
✔ Use automation – Automatically notify sales when a lead reaches a score threshold.
✔ Refine based on feedback – Regularly review which leads actually converted.

Companies that align sales and marketing on lead scoring see 208% higher revenue growth from priority leads (Marketo).

So, is your business ready to move beyond outdated lead scoring and start using intent data to drive better conversions?

Now’s the time to upgrade your strategy and start focusing on the leads that truly matter.

What is the Difference Between a Priority Matrix and a Scoring Model?

Many companies mistakenly treat these as interchangeable. So how do you use it best. Use a priority matrix for quick visual classification and a scoring model for automated ranking and decision-making.

Lead Scoring Framework: How It All Comes Together

A strong lead scoring framework combines:

Data Collection – Pulling insights from CRM, website tracking, and intent platforms.
Scoring Criteria – Assigning points based on engagement and fit.
Thresholds & Qualification – Determining when a lead becomes an SQL.
Automation & AI – Continuously refining scores based on real-time data.

Final Thoughts: Is Your Lead Scoring Model Built for Conversion?

If your current model isn’t tracking real-time intent, it’s time to rethink your strategy.

Start aligning intent data, ICP fit, and AI-driven scoring to make sure your leads aren’t just good fits, but ready to buy.

Sit down with your leadership, refine your scoring strategy, and give your sales team the competitive edge they need. Your entire organization will thank you for it!

Vikas Bhatt

Vikas Bhatt is the Co-Founder of ONLY B2B, a premium B2B lead generation company that specializes in helping businesses achieve their growth objectives through targeted marketing & sales campaigns. With 10+ years of experience in the industry, Vikas has a deep understanding of the challenges faced by businesses today and has developed a unique approach to lead generation that has helped clients across a range of industries around the globe. As a thought leader in the B2B marketing community, ONLY B2B specializes in demand generation, content syndication, database services and more.