CHURN PREDICTION

The problem of customer churn is a continuous challenge for profitability in the telecommunications sector, particularly given significant competition within the industry. Therefore, the ability to use advanced analytics to identify compressed subscribers in advance, and potentially retain them, is critical. This study describes an end-to-end explainable AI framework for predicting and intervening with telco churn. We used the IBM Telco Customer Churn dataset and simulated relevant behavioural features, so we could train our system via informative preprocessing (handling missing values and inappropriate encodings), feature engineering (where we devised aspects such as charge ratio, tenure, and service stability), and XGBoost modelling (where our modelling is optimized via stratified cross-validation and business-based target metrics) to monitor our predictions. Importantly, we provide SHAP-based explainability for every prediction, where our predictions contain both global and local explainability features to provide an actionable bridge between the power of algorithmic manipulation and business consideration for the customer. Our prediction offers the functionality of a business logic layer allowing churn probabilities to be mapped to risk formulations, loyalty tier definitions, and ways to intervene with the customer prior to their leaving as well as knowing later via a secure Flask-based web app with live analysis and audit of actions performed. Our experiment produced strong prediction values (AUC-ROC value of 0.95; retention of churn class recall up to 0.93), consistent with feature ranking providing interpretably relevant information, and identifiable space for practical business consideration. In conclusion, we have provided what we believe to be a scalable and transparent model for interpretable churn management with an aim of providing timely, informed decisions and accountability to operate the retention strategy in the telecommunications environment.