Churn Prediction

Churn prediction uses machine learning to identify customers at risk of leaving before they actually churn. This enables proactive retention interventions that are far more effective than reactive win-back campaigns.

Churn prediction signals:

• Declining engagement/usage
• Support ticket patterns
• Payment issues
• Negative sentiment
• Competitor activity
• Contract milestones

ML approaches:

• Classification models: Predict churn probability
• Survival analysis: Predict when churn might occur
• Deep learning: Pattern detection in complex data
• Ensemble methods: Combining multiple models

Implementation considerations:

• Define churn clearly for your business
• Choose prediction window (30, 60, 90 days)
• Balance precision and recall
• Integrate with CRM/action systems
• Measure intervention effectiveness