Customer retention intelligence
Churn Prediction ML
A machine-learning pipeline that predicts customer churn before it happens, flags high-risk accounts with ensemble methods and surfaces feature-level explanations to drive targeted retention campaigns.
- Engagement
- Applied AI delivery
- System type
- Customer retention intelligence
- Primary outcome
- Operational leverage
Challenge
The existing operating model made customer retention intelligence slow to inspect, difficult to scale and dependent on manual coordination.
Response
End-to-end preprocessing, EDA and feature engineering on behavioural data. Logistic Regression, Random Forest and XGBoost with hyperparameter tuning
Result
Reduces churn by enabling proactive, data-driven retention — focusing resources on the customers most likely to leave and most valuable to keep.
System architecture
From signal to controlled action.
01
End-to-end preprocessing, EDA and feature engineering on behavioural data
02
Logistic Regression, Random Forest and XGBoost with hyperparameter tuning
03
SHAP-based feature importance revealing the top churn drivers
04
Real-time risk scoring and cohort segmentation for campaign targeting
Business value
The system changes the operating baseline.
Reduces churn by enabling proactive, data-driven retention — focusing resources on the customers most likely to leave and most valuable to keep.
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