revtrace

Platform module

Scoring Model Health

14 automated checks to evaluate lead scoring models, threshold calibration, score distribution, and scoring-to-lifecycle alignment.

14 automated checks
app.revtrace.ai/audits

Audit results

14 checks · live

Warning

Threshold calibration

Your MQL threshold is 60 but the median converted lead scored 42.

Warning

Score-to-conversion correlation

Lead score has weak correlation (0.18) with deal conversion.

Info

Score decay configuration

No score decay rules — engagement from 6+ months ago carries full weight.

+11 more findings

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Why does lead scoring accuracy matter?

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.

Warning

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.

Warning

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.

Info

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 14 checks in this module

Featured findings above are 3 of the most impactful. Here's the complete list.

Score distribution analysis
Scoring property coverage
Behavioral signal mapping
Demographic signal mapping
MQL threshold effectiveness
Score inflation detection
Unused scoring properties
Scoring model completeness
Activity recency weighting
Score segmentation gaps
Lead grade alignment
Scoring automation integration
Model refresh frequency

Questions

Scoring audit questions

How do I know if my HubSpot lead scoring model is working?

Test it against outcomes, not intuition: if high-scoring leads do not convert at a higher rate than low-scoring leads, the model is not predictive. RevTrace checks score-to-conversion correlation, score distribution across your database, threshold calibration, and whether scores actually drive the lifecycle transitions they are supposed to.

What is a good MQL score threshold?

The right threshold is the point where conversion rate rises sharply, which is specific to your data — a copied threshold from another company is arbitrary. A useful warning sign is distribution: if most of your database clusters just above or just below the line, the threshold is set where it cannot discriminate between leads.

What is score decay in lead scoring?

Score decay reduces a lead's score as their activity ages, so someone who downloaded an ebook a year ago does not look as engaged as someone who did it yesterday. Without decay, scores only ever climb and your MQL pool fills with stale leads. RevTrace checks whether decay is configured and whether the window is realistic for your cycle.

See the problems this module solvesView solutions

Scoring module

14

automated checks

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