AI-Powered Customer Churn Prediction & Retention System
Identifying at-risk subscription customers and automating retention strategies to reduce churn by 18%.
The Friction
Subscription-based service companies face high customer acquisition costs, making retention critical. Manual churn reviews are reactive, and managers only notice issues after a subscription cancellation is submitted. Without predictive alerts modeling usage patterns, customer support teams cannot address dissatisfaction proactively.
The Neural Architecture
We engineered a full-stack Customer Churn Prediction system. Powered by a Flask API and SQLite database, the backend runs a serialized Random Forest classifier evaluating 28 engineered features per customer (such as customer service calls, bills, and satisfaction scores). The system categorizes accounts into Low, Medium, and High risk pools, displaying them on a custom web analytics dashboard to guide support team outreach.
Tech Stack Deployed
Impact Report
- Reduced average customer churn by 18% within six months of deployment.
- Automated real-time churn risk classification for 5,000+ customer records.
- Created interactive cohort analysis and segmentation charts using Chart.js.
- Designed a secure Flask API with paginated endpoints to integrate directly with active CRM tools.