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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 your custom Python lead scoring model against HubSpot’s manual scores requires comprehensive data comparison and outcome tracking. Without proper validation, you can’t determine which approach better identifies qualified leads or justify the investment in custom models.
Here’s how to build a complete validation framework that compares both scoring methods against actual conversion outcomes.
Build comprehensive scoring validation and comparison using Coefficient
Coefficient provides the perfect platform for importing both score sets, creating comparison frameworks, and tracking which model better predicts conversions. You can analyze correlation, accuracy, and performance differences while monitoring score stability over time.
How to make it work
Step 1. Import both scoring datasets with outcomes.
Pull contacts with HubSpot’s manual lead scores, your Python model scores stored in custom properties, and conversion outcomes (became customer, opportunity created). Include engagement metrics and timeline data for context analysis.
Step 2. Create comparison formulas for agreement analysis.
Build agreement tracking:. Calculate correlation between methods:to measure overall alignment.
Step 3. Build validation metrics against actual outcomes.
Create accuracy comparisons showing which model better predicts conversions. Calculate false positive rates (high scores that don’t convert) and false negative rates (low scores that do convert) for both approaches. Track lift analysis measuring improvement in top decile identification.
Step 4. Set up automated testing and monitoring.
Schedule weekly imports of newly scored leads to track ongoing performance. Monitor score drift over time and set up alerts when model agreement drops below 70%. Use Coefficient’s Snapshots to preserve historical scores for longitudinal analysis.
Step 5. Create A/B testing framework.
Randomly assign leads to each scoring method and track conversion outcomes. Export validation results back to HubSpot for sales team feedback. Create automated Slack alerts highlighting cases where models significantly disagree for manual review.
Prove your model’s value with data
Proper validation typically reveals that Python models identify 40-60% more qualified leads that manual scoring misses, while providing clear documentation of where each approach excels. Coefficient makes it easy to build comprehensive validation frameworks and track model performance over time. Start validating your scoring models today.
