Services
Marketing
Lead Scoring Optimization
Evaluate your lead scoring model against actual conversion data and rebuild it to predict real pipeline.
Intelligence
The moment you know a lead scoring model needs rebuilding is when sales stops acting on it. Not because they're lazy, but because they've been burned enough times by high-scored leads that never went anywhere. When we pull the historical data, that instinct is almost always validated: the scoring criteria don't correlate with close rates. They correlate with engagement, which sounds like the same thing but isn't. This engagement rebuilds the model on actual revenue outcomes rather than activity signals.
Lead scoring models degrade over time. They were built on assumptions about what a qualified buyer looks like, validated (if at all) against a much smaller dataset, and haven't been touched since. Meanwhile, your ICP has shifted, your product has changed, and the behavioral signals you're capturing now are different from what you were capturing when the model was set up.
The most common symptom: sales doesn't trust the scores. They've been burned too many times by high-scored leads that never converted, and they've closed deals that marketing scored as low-priority. When sales stops acting on scores, the whole system stops working, and you're left with a scoring model that consumes resources and influences nothing.
What we do
We pull your historical lead score data alongside closed-won and closed-lost outcomes and run a correlation analysis. For each scoring criterion, we calculate how strongly it predicts actual conversion. Some criteria will show strong predictive value. Others will show no correlation at all, or negative correlation, meaning high scores on that criterion actually correlate with lower close rates.
We rebuild the model based on what the data shows, not what seemed logical at implementation. Then we build the monitoring framework so it doesn't drift again.
For context on what this type of analysis typically surfaces, read how source bias corrupts scoring from the start.
Deliverable
A scoring audit report with criterion-level predictive analysis, a rebuilt scoring model with validated weights, an implementation guide, and an A/B test plan for rolling it out. Plus a monitoring framework for recalibrating quarterly.
Outcome
A scoring model that sales uses because it reflects reality. Higher conversion rates from scored leads. Cleaner MQL thresholds. And a process for keeping it accurate over time, which most companies skip the first time around.
How Stratum Group Cut Reporting Time by 50% and Detected Churn 60 Days Earlier — cut decision time by 50% and started detecting churn 60 days earlier.
See how it worked in practice: Nova Lending built a lead scoring model that sales actually used.
Best Fit
If your lead scoring model hasn't been validated against outcomes in the past six months, it's running on assumptions. If sales routinely ignores marketing-qualified leads or if your top-scored leads don't show up in closed-won data, this is the engagement that fixes the root cause rather than the symptoms.