Your Lead Scoring Model Is Probably Lying to You

Here's the uncomfortable truth: most B2B lead scoring models have almost no correlation with actual closed deals.

You've watched it happen. Marketing celebrates passing over "highly qualified" leads. Sales works them for weeks. Nothing closes. Meanwhile, a lead with a mediocre score converts in three days because someone actually had budget and urgency - traits your scoring model never captured.

The problem isn't that lead scoring doesn't work. It's that most models are built on the wrong foundation. They score engagement - webinar attendance, content downloads, email opens - and assume engagement equals buying intent.

It doesn't.

The pattern we see repeatedly: companies invest months building sophisticated scoring systems, then abandon them within a year because sales stops trusting the numbers. Not because the concept failed, but because nobody validated whether high scores actually predicted revenue.

Why Engagement-Based Scoring Fails

The logic seems sound: leads who engage more are more interested, so they should score higher. But this conflates two completely different things.

Fit tells you whether someone could buy - do they have the budget, authority, need, and timeline? Intent tells you whether they're trying to buy - are they actively evaluating solutions?

A marketing intern downloading your ebook for a research project has high engagement and zero fit. A VP of Operations who visits your pricing page once at 11pm has low engagement and extremely high intent.

Most scoring models weight engagement too heavily because it's easy to measure. You can track every click, every download, every email open. Fit data requires integration with enrichment tools or manual research. Intent signals require understanding which behaviors actually matter.

So teams default to what they can measure, not what predicts outcomes.

The Validation Process Nobody Does

Before you rebuild your scoring model, you need to understand where it's actually failing. This requires something surprisingly few companies do: comparing scores against closed-deal data.

Pull your last 100 closed-won deals. Look at what their lead scores were when sales first engaged them. Then pull 100 closed-lost opportunities and do the same comparison.

If your scoring model works, closed-won leads should have meaningfully higher scores at the point of first sales contact than closed-lost leads. If there's no significant difference - or worse, if closed-lost leads scored higher - your model has no predictive power.

"If high-scoring leads often result in sales, the model works well. Otherwise, it might need tweaking." - Intelemark

This sounds obvious. But ask yourself: when was the last time anyone at your company ran this analysis?

What to Look for in the Data

Beyond the aggregate comparison, segment your closed deals by the attributes your model scores. If you give points for company size, check whether larger companies actually convert at higher rates. If you score for job title, verify that VPs close more often than managers.

You'll likely find that some attributes strongly correlate with closed deals while others have no relationship at all. Some might even have negative correlations - attributes you're rewarding that actually indicate lower likelihood to buy.

One common finding: content engagement often has weak or no correlation with closed deals, while pricing page visits and demo requests correlate strongly. This suggests a simple fix: stop scoring content downloads, start scoring commercial intent behaviors.

Rebuilding Around What Actually Predicts Revenue

Once you know which attributes correlate with closed deals, rebuilding the model becomes straightforward. But the approach matters.

Start with fit, layer in intent. Firmographic fit should be your foundation - industry, company size, tech stack, geography. These don't change based on marketing campaigns or random website browsing. A well-fit lead stays well-fit whether they engage or not.

Intent signals then modify the score based on buying behavior. Not all engagement is intent. Visiting the blog isn't intent. Attending a webinar might not be intent. But comparing your product to competitors, viewing pricing, requesting a demo, or asking specific implementation questions - these indicate someone is actively trying to make a purchase decision.

Weight based on evidence, not intuition. If your data shows that leads from companies with 50-200 employees close at twice the rate of larger enterprises, weight company size accordingly - even if your sales team insists bigger is always better. The numbers don't lie.

Include negative scoring. Some behaviors should reduce scores. Free email domains, job titles indicating individual contributors without purchasing authority, industries you've consistently failed to close. These save sales time by keeping bad-fit leads from bubbling up.

A Simpler Model Often Wins

There's a temptation to make scoring complex - dozens of attributes, intricate point systems, decay algorithms. But complexity creates fragility. Every variable you add is another thing that might not correlate with outcomes, another thing to maintain, another way for the model to drift.

What we've found works better: fewer attributes with stronger correlations. A model with five high-signal attributes outperforms a model with fifty weak signals. Fit on company type and size. Intent on pricing page and demo requests. Maybe one or two behavioral signals you've validated actually matter.

You can always add complexity later. You can't easily debug a complex model that's giving bad results.

Making Sales Trust the Numbers Again

A technically sound scoring model means nothing if sales ignores it. And if they've been burned before - handed "hot" leads that went nowhere - they will ignore it.

Rebuilding trust requires transparency about what changed. Walk through the validation analysis with sales leadership. Show them the closed-deal correlation data. Explain exactly what the new model scores and why.

Then set a threshold that actually means something. "Marketing qualified" should indicate genuine likelihood to buy, not just "engaged with our content." If a lead hits the threshold, sales should expect that lead to close at a meaningfully higher rate than leads below the threshold.

Research shows that 68% of efficient marketers report lead scoring as a primary driver of revenue contribution - but only when the scoring accurately reflects buying potential. Genroe

Metrics That Prove It's Working

Stop measuring lead volume passed to sales. That's a vanity metric that encourages gaming the system. Instead, track:

Conversion rate by score band. Leads scoring 80+ should convert at higher rates than leads scoring 60-79. If they don't, something's wrong.

Sales accepted rate. What percentage of marketing-qualified leads do sales actually work? If reps are ignoring high-scoring leads, either the scores are bad or the threshold is too low.

Velocity by score. High-scoring leads should close faster. If your best-scored leads have the same sales cycle length as average leads, scoring isn't capturing urgency correctly.

Win rate on scored vs unscored. This is your ultimate validation. Leads that came through your scoring process should win at higher rates than leads from other sources.

Recalibration Isn't a One-Time Event

Your market changes. Your product changes. Your customer profile changes. A scoring model built on last year's closed deals might not predict this year's buyers.

Build in a quarterly review. Re-run the closed-deal analysis. Check whether correlations still hold. Adjust weights based on fresh evidence.

This doesn't have to be complex. An hour of analysis each quarter prevents the slow drift that eventually destroys trust in the system.

FAQ

How long does it take to see if a new scoring model is working?

You need enough closed deals to compare. If you close 20 deals per month, wait two to three months before drawing conclusions. For smaller deal volumes, you might need six months of data. Don't panic-adjust based on a handful of outcomes.

Should we score leads differently for different products or segments?

Yes, if the buying patterns differ. An enterprise motion and an SMB motion often have completely different fit and intent signals. Running a single model across both usually means neither works well. Start with your highest-volume segment and build separate models only when you have enough closed-deal data to validate each.

What's the minimum data we need to build a predictive model?

You can start validating with 50-100 closed deals, though more is better. If you have fewer than 50 closed deals total, focus on fit-based scoring using industry knowledge until you have enough data to validate empirically.

How do we handle scoring when leads engage but never talk to sales?

High engagement with no sales conversation often indicates content consumers rather than buyers. Consider capping how much engagement alone can increase a score - a lead shouldn't reach "sales-ready" purely through content consumption without any commercial intent signals.

Most lead scoring problems aren't technology problems. They're validation problems. You built a model on assumptions about what predicts revenue, but never checked whether those assumptions hold.

If your sales team has lost faith in lead scores, or if your marketing-sales handoff feels broken, the fix starts with data: compare what you're scoring against what actually closes. Everything else follows from that.

Need help auditing your current lead scoring model or building automation that connects the pieces? Talk to AlusLabs about a consultation - we help B2B teams build systems that actually predict revenue, not just measure activity.