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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.”
HubSpot Professional’s manual lead scoring typically achieves 60-70% accuracy in predicting conversions, while well-tuned scikit-learn models can reach 80-85% accuracy. But proving this improvement requires the right data infrastructure for validation.
Here’s how to set up accurate comparisons between manual scoring and custom ML models to quantify the performance difference.
Build model comparison infrastructure using Coefficient
Coefficient provides the data foundation needed to validate both manual scoring accuracy and custom model performance against actual conversion outcomes. You can track historical performance, run A/B tests, and create real-time accuracy dashboards.
How to make it work
Step 1. Capture historical lead data with Snapshots.
Use Coefficient’s Snapshots feature to capture HubSpot lead data at different time points. This creates ground truth datasets that show how both manual scores and ML predictions performed against actual conversion outcomes over time.
Step 2. Set up real-time comparison dashboards.
Import live HubSpot lead data alongside your custom Python scoring results in Google Sheets. Create side-by-side accuracy comparisons tracking precision, recall, and F1-scores for both approaches in real-time.
Step 3. Design A/B testing infrastructure.
Use filtered imports to create different lead segments for testing. Apply manual scoring to one cohort while using scikit-learn models on another, then compare conversion rates and sales velocity between groups.
Step 4. Import comprehensive data for feature engineering.
Pull engagement history, email interactions, and website behavior data to feed into your scikit-learn models. This ensures fair comparison by giving both approaches access to the same underlying data.
Step 5. Analyze complex interaction effects.
Custom models excel at detecting patterns like “enterprise prospects from manufacturing who engage with pricing content convert 3x higher.” Manual scoring rules can’t capture these multiplicative effects that drive the accuracy improvement.
Prove your model’s value with data
The accuracy improvement becomes clear when you have proper validation infrastructure. Custom models consistently outperform manual scoring by identifying complex patterns that simple point systems miss. Start comparing your approaches with reliable data today.
