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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
Validating custom lead scoring models requires comprehensive historical data linking lead scores to actual conversion outcomes. HubSpot’s standard reports can’t easily correlate lead scores at specific time points with eventual deal closures across different time horizons.
Here’s how to build the data infrastructure essential for robust model validation and statistical accuracy measurement.
Build comprehensive model validation infrastructure using Coefficient
Coefficient provides the data foundation for rigorous model validation by capturing historical scoring data, mapping conversion outcomes, and enabling statistical testing that goes far beyond HubSpot’s basic conversion reporting.
How to make it work
Step 1. Capture historical score tracking with Snapshots.
Use Coefficient’s Snapshots feature to capture lead scores and contact properties at regular intervals (weekly/monthly). This creates a time-series dataset showing how scores evolved before conversions occurred, which is crucial for validating predictive accuracy.
Step 2. Map conversion outcomes to historical scores.
Import HubSpot deal data with associated contacts, including deal creation dates, close dates, and deal values. Cross-reference this with historical lead scores to calculate precision, recall, and ROI metrics for different score thresholds.
Step 3. Set up cohort analysis for model consistency.
Create filtered imports for different lead segments by source, industry, and company size. This validates whether your scoring model works consistently across various cohorts or has bias toward specific lead types.
Step 4. Analyze time-to-conversion patterns.
Import comprehensive engagement data and deal progression history to validate whether high-scoring leads actually convert faster and with higher deal values, not just higher conversion rates.
Step 5. Build A/B testing frameworks for model comparison.
Set up parallel scoring approaches using different model versions, then track conversion performance over time to statistically validate which approach delivers better business outcomes.
Prove your model’s predictive power
The key advantage is creating a complete audit trail from initial lead score through final conversion outcome, enabling statistical validation of model performance with real business impact data. Start validating your scoring models with comprehensive conversion analysis.
