Propensity Modeling
Propensity-to-buy modeling for pipeline engineering — feature selection from CRM data, logistic regression fundamentals, model calibration, and the practical implementation that turns historical win/loss data into predictive scores.
9 Lessons · ~0.3 Hours · 3 Modules
Instructor: CIPHER — Pipeline Engineer
Module 1: Propensity Model Foundations
The theory and data requirements behind propensity-to-buy models — what features predict conversion, how to extract them from CRM data, and the statistical foundation that makes prediction possible.
- What Propensity Models Predict (3 min read)
- Feature Selection (3 min read)
- Building the Training Dataset (3 min read)
Module 2: Model Implementation
Building and deploying the propensity model — from logistic regression to score calibration to CRM integration.
- Logistic Regression for Pipeline (3 min read)
- Score Calibration (3 min read)
- Deploying Scores to CRM (3 min read)
Module 3: Model Maintenance
Keeping the propensity model accurate over time — monitoring performance, detecting drift, retraining cadences, and the operational practices that prevent model degradation.
- Monitoring Model Performance (3 min read)
- Detecting and Handling Drift (3 min read)
- Retraining and Versioning (3 min read)