📘

Customer Churn Prediction System

Design a system that predicts which customers are likely to cancel their subscription.

Scenario: A SaaS company wants to identify at-risk customers before they churn, so the retention team can intervene proactively. The system should predict churn probability and explain the key risk factors.

Your Task: Design the churn prediction pipeline.

Your design should address:

What data signals indicate churn risk
How to handle class imbalance (churners are rare)
Feature importance for actionable insights
When and how to refresh predictions

Design Mode

📝 Your Design Approach

Describe your system design approach. Consider components, data flow, and key decisions.

🎯 Design Questions(Select all that apply)

Q1.Which features are most predictive of customer churn?

Q2.What is the right way to define the prediction window for churn?

Q3.How should the system handle class imbalance (most customers don't churn)?

Q4.What intervention strategy should accompany the churn model?

0 of 4 questions answered

📘