End-to-End Customer Churn Prediction System with Automated Retention Workflows

Challenge

The company had high customer attrition and no proactive way to identify at-risk customers. Retention was reactive, and there was no systematic way to prioritize interventions or coordinate them across the contact center, business and onboarding teams.

Approach

• Built a machine learning pipeline that predicts churn probability from transaction history, engagement metrics, support interactions and demographic features.
• Engineered behavioral indicators such as declining transaction frequency, support ticket patterns and product usage trends.
• Trained the model with hyperparameter optimization.
• Designed a workflow that segments at-risk customers into High, Medium and Low risk tiers, each with its own intervention strategy.
• Built integrations that send churn predictions and recommended actions to the CRM systems different teams use.
• Built dashboards for contact center agents with customer context, churn probability and suggested retention offers, plus feedback loops that track intervention results.

Results

• Targeted, prioritized retention outreach instead of reactive offers.
• Automated workflow assignments and priority scoring across teams.

Technologies

Python (scikit-learn, XGBoost, pandas, NumPy), Tableau, SQL, Docker.

Impact

Retention moved from reactive to predictive, with each team working from the same risk scores and next actions.