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An analytics tool that predicts which customers are likely to stop using a service and shows the reasons, helping businesses plan retention offers.
Problem Statement
Acquiring a new customer costs far more than retaining one, yet most small businesses find out about churn only after the customer has left.
Objectives
Analyse customer behaviour and usage patterns
Train and compare churn prediction models
Explain the key drivers of churn per customer
Build a dashboard with retention recommendations
Modules
Data Preparation
Cleaning, feature engineering, and customer segmentation.
Prediction Models
Logistic regression, random forest, and boosting with evaluation reports.
Explainability
Feature importance and per-customer reasons using SHAP values.
Business Dashboard
At-risk customer lists, segment views, and campaign tracking.
Expected Output
A dashboard that ranks customers by churn risk, shows why each is at risk, and tracks the outcome of retention actions.
Future Scope
Customer lifetime value prediction, automated email campaigns, and uplift modelling.