Churn Prediction Workflow

• Description:

  Automates the identification of customers at risk of churn by analyzing behavioral, transactional, and engagement data. The system uses predictive analytics or machine learning models to score customers based on likelihood to cancel or disengage, then triggers targeted retention actions such as personalized offers, support outreach, or loyalty campaigns.

• Flow:

  CRM/Database → Collect customer data (usage frequency, support tickets, billing history, engagement metrics) → Run churn prediction model → Assign churn risk score (low/medium/high) → Trigger automated workflows (discount offers, personalized email, proactive support call) → Update CRM with risk status → Track outcomes and continuously retrain model.

• Use Case:

  SaaS companies, subscription businesses, e-commerce platforms, and service providers aiming to proactively reduce customer churn and improve retention rates by acting before cancellation happens.

• Value:

  Reduces revenue loss, improves customer lifetime value (CLV), strengthens loyalty, ensures proactive engagement with at-risk customers, and provides data-driven insights for improving product/service experiences.