How ML Predicts Customer Churn 30 Days Before It Happens

A B2B SaaS platform with $18M ARR was losing 15% to churn annually. After implementing ML churn prediction: • 71% of predicted churns were prevented • $4.2M in annual revenue saved • 30-day average warning time • 89% prediction accuracy The key: They did not just predict churn—they acted on it with targeted interventions based on churn reasons identified by the model.

Understanding when and how churn signals appear is crucial for intervention: 30 Days Before: First subtle signals appear - Login frequency drops 15% - Feature usage narrows - Payment method expires soon - Risk Score: 62% 14 Days Before: Signals intensify - Support ticket sentiment turns negative - Team members removed - API usage drops 40% - Risk Score: 78% 7 Days Before: Critical indicators - Billing page visits increase - Competitor pricing pages in browser history - Data exports spike - Risk Score: 91% Each stage requires different intervention strategies. Early warnings need gentle engagement. Late warnings need executive escalation.

Our churn prediction uses ensemble learning combining four specialized models: 1. Gradient Boosting: Captures non-linear payment patterns 2. LSTM Networks: Identifies temporal usage sequences 3. Random Forests: Handles categorical feature interactions 4. Neural Networks: Detects complex multi-signal patterns These models are retrained daily on your specific data, improving accuracy over time. After 90 days, most customers see 91-94% accuracy.