Scoring Module

The Scoring module audits your lead and company scoring models to ensure they're accurately identifying high-intent prospects and properly prioritizing sales outreach.

What it checks

The module runs 14 checks covering:

• Score distribution — Are scores meaningfully distributed or clustered?
• Score-to-conversion correlation — Do higher scores actually convert better?
• Stale scoring criteria — Properties used in scoring that are rarely populated
• Score inflation — Scores trending upward without corresponding conversion improvement
• Model coverage — Percentage of contacts with meaningful scores
• Threshold alignment — Are MQL thresholds aligned with actual conversion data?

Common findings

• Scores clustered at extremes — Everyone is scored very high or very low, losing differentiation
• No correlation between score and conversion — The model isn't predictive
• Scoring criteria based on empty fields — Properties that aren't populated can't contribute signal

Why it matters

Lead scoring is how marketing tells sales "this one is ready." When scoring models are inaccurate, sales wastes time on low-quality leads while high-intent prospects go cold. A well-tuned scoring model is the difference between efficient pipeline generation and random outreach.