Why Your Lead Scoring Model Is Lying to You (And How to Fix It)

Most lead scoring models reward form fills and email opens. The buyers who actually close are doing something completely different. Here's how to build a model that predicts revenue instead of just activity.

The problem with scoring what's easy to measure

The default lead scoring model gives points for email opens, form submissions, page views, and webinar registrations. It penalises inactivity. It resets on unsubscribes. It feels logical — more engagement equals more interest.

The problem is that most of this activity is noise. People open emails on autopilot. Forms get filled for content that has nothing to do with buying intent. Webinars attract curious people who will never convert.

What actual buyers do differently

When we overlay lead score data with closed-won deals, the pattern is consistently different from what most models are scoring. High-converting leads tend to:

• Visit pricing pages (multiple times, often late at night)
• Read case studies for companies similar to themselves
• Engage with comparison content ("X vs Y")
• Invite colleagues to review content (forwarded emails, shared links)
• Show intent signals in third-party data (G2 reviews, Bombora topics)

Almost none of these behaviours are captured in a standard Marketo or HubSpot lead scoring setup.

The three-layer scoring model that actually works

Effective lead scoring requires three separate dimensions, weighted and combined:

1. Fit score — How closely does this person match your ICP? Company size, industry, tech stack, role seniority, geography. This is firmographic and technographic data, not behavioural. It should be stable and not decay.

2. Intent score — Is this person in an active buying cycle? This is where third-party intent data (Bombora, G2, Demandbase) becomes essential. Fit without intent is a cold lead. Intent without fit is a bad fit.

3. Engagement score — Are they engaging with high-value, late-stage content? Not email opens — pricing pages, ROI calculators, case studies, comparison pages.

How to build it without starting from scratch

If you're in Marketo or HubSpot, you don't need to rebuild your scoring model from scratch. You need to audit what's currently being scored, weight the high-signal behaviours more heavily, and connect an intent data source.

The typical implementation takes four to six weeks: one week to audit existing model and close-won data, two weeks to rebuild scoring rules, one week to QA, and two weeks of parallel running before cutover.

What changes when your model is accurate

When your lead scores actually predict buying intent, sales routing becomes reliable. SDRs stop chasing cold leads with high scores. Hot prospects don't sit in nurture queues for weeks. The conversation between marketing and sales shifts from "your leads are junk" to "we agree on who to prioritise."