Overview
Most churn prediction fails because teams build models that predict churn accurately but do not prevent it. They identify at-risk customers too late, or they intervene with everyone instead of prioritizing high-value customers.
The Churn Prediction Model builds actionable prediction systems with early warning indicators (30-60 days before churn), intervention strategies prioritized by customer LTV, and A/B testing methodology to validate that interventions actually reduce churn.
What you get: - Feature engineering for churn prediction - Model training and evaluation methodology - Churn risk scoring and segmentation - Intervention strategy by customer value tier - A/B testing framework for retention campaigns - Model performance tracking and improvement
Built for: customer success teams, retention marketers, and data scientists who need churn prediction that drives retention — not just accurate forecasts that do not change outcomes.