Lilly Tech Systems
Churn Prediction Models
From simple classifiers to deep learning sequence models, learn the algorithms that power modern churn prediction. Understand when to use classification models vs. survival analysis and how to handle the unique challenges of churn data.
Classification Approaches
| Algorithm | Strengths | Best For |
|---|---|---|
| Logistic Regression | Interpretable, fast, good baseline | Initial models, regulated environments |
| Random Forest | Handles nonlinearity, robust to outliers | Medium-sized datasets with mixed features |
| XGBoost / LightGBM | Top accuracy, handles missing data | Production systems needing best accuracy |
| LSTM / Transformer | Captures temporal patterns in sequences | Rich event stream data, large datasets |
Survival Analysis for Churn
Unlike classification which predicts whether a customer will churn, survival analysis predicts when they will churn:
- Cox Proportional Hazards: Identify which features accelerate or delay churn without assuming a specific time distribution
- Kaplan-Meier Estimator: Visualize churn probability curves for different customer segments over time
- Random Survival Forests: Combine the flexibility of tree-based models with survival analysis to capture nonlinear effects
- Deep Survival Models: Neural networks for survival analysis that handle complex feature interactions and temporal dependencies
Binary Classification
Predict churn yes/no within a fixed window (e.g., next 30 days). Simple to implement but loses timing information and requires choosing a prediction window.
Survival Analysis
Predict the probability of churn over continuous time. Handles censored data (customers who have not yet churned) and provides time-to-churn estimates.
Multi-Horizon
Predict churn probability at multiple time horizons (7, 30, 60, 90 days). Provides the most actionable output for tiered intervention strategies.
Sequence Models
Process the ordered sequence of customer events to learn temporal patterns. LSTMs and Transformers excel when you have rich event-level data.
Handling Churn Data Challenges
- Class Imbalance: Churn rates of 2-5% mean heavily imbalanced data. Use SMOTE, class weights, or focal loss to address this
- Defining Churn: For subscription businesses, churn is clear (cancellation). For transactional businesses, define inactivity thresholds carefully
- Prediction Window: Choose a window that gives enough lead time for intervention (30-90 days) while maintaining prediction accuracy
- Feature Leakage: Ensure no features contain information about the churn event itself (e.g., cancellation page visits in the prediction window)
- Temporal Validation: Always use time-based train/test splits. Never randomly shuffle churn data, as this creates unrealistic future data leakage