Design a Customer Churn Prediction System — System Design Interview Practice
Design an ML system that predicts customer churn probability, identifies at-risk segments, determines churn drivers, and integrates with retention campaign tools for proactive engagement. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Predict churn probability for each customerRequirement
- Identify key drivers of churnRequirement
- Segment customers by churn risk levelRequirement
- Integrate with CRM and marketing automationRequirement
- Track model predictions vs actual outcomesRequirement
- Generate customer lifetime value estimatesRequirement
- Trigger automated retention workflowsRequirement
- Provide dashboards for business stakeholdersRequirement
- mlConcept to explore
- churn predictionConcept to explore
- customer analyticsConcept to explore
- classificationConcept to explore
- marketingConcept to explore
- retentionConcept to explore