Ml
4 posts
Clay's AI scoring is still rules-based. Here's what's missing.
Prompt-based lead scoring in Clay workflows is still rules-based, just written in natural language. Here's how a predictive API endpoint upgrades the scoring step from subjective to statistical.
Read postFeature Stability Score: the metric your model's explainability is missing
Accuracy tells you the model works. SHAP tells you why. Feature Stability Score tells you whether that why will still hold next quarter. A 2026 paper introduced the formula. Here's why it matters for GTM scoring.
Read postHow a B2B software company used ML scoring to fix its pipeline
A B2B software company replaced its traditional lead scoring with a Gradient Boosting model trained on 4 years of CRM data. 98.39% accuracy. Here's what happened.
Read postWhy rules-based lead scoring fails, and what to replace it with
Rules-based scoring is an opinion in a spreadsheet. ML scoring is what your data actually shows. A case study from a B2B software company with 16,600 lead records explains why.
Read post