Platform module
Scoring Model Health
17 automated checks to evaluate lead scoring models, threshold calibration, score distribution, and scoring-to-lifecycle alignment.
Audit results
17 checks · live
Threshold calibration
Your MQL threshold is 60 but the median converted lead scored 42.
Score-to-conversion correlation
Lead score has weak correlation (0.18) with deal conversion.
Score decay configuration
No score decay rules — engagement from 6+ months ago carries full weight.
+14 more findings
Why it matters
Lead scoring is the bridge between marketing and sales. When scores don't reflect buyer intent, sales wastes time on unqualified leads while hot prospects go cold. Miscalibrated thresholds mean your MQL definition is meaningless — and pipeline quality suffers.
Sample findings
Examples from a real scoring audit
Every check produces a finding with severity, an impact estimate, and a step-by-step fix.
Threshold calibration
Your MQL threshold is 60 but the median converted lead scored 42.
Estimated Impact
Sales missing 38% of historically high-converting leads.
Solution
Recalibrate MQL threshold based on actual conversion data.
Score-to-conversion correlation
Lead score has weak correlation (0.18) with deal conversion.
Estimated Impact
Scoring model not predicting buyer intent.
Solution
Review scoring properties and rebalance behavioral vs demographic signals.
Score decay configuration
No score decay rules — engagement from 6+ months ago carries full weight.
Estimated Impact
Stale leads still surfacing as MQLs.
Solution
Add a score decay rule for engagement properties older than 90 days.
All 17 checks in this module
Featured findings above are 3 of the most impactful. Here's the complete list.
Scoring module
17
automated checks
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