Customer Analytics · Retention Modeling
Predictive churn analytics for early risk detection and personalized customer retention.
Customer churn represents a significant risk for retail banks, as acquiring new customers is substantially more costly than retaining existing ones. Traditional retention strategies often rely on reactive measures or broad marketing campaigns that fail to identify the customers most likely to leave. Without predictive insight into churn drivers and early warning signals, banks struggle to allocate retention resources effectively. The challenge is to detect at-risk customers in advance and translate behavioral signals into targeted, data-driven retention actions.
A structured customer analytics pipeline was implemented to predict churn risk and uncover the key drivers behind customer attrition. The approach combined data preprocessing, feature engineering, and model benchmarking to compare classical statistical models with ensemble machine learning methods. Performance was evaluated on unseen test data to ensure reliable generalization, while feature importance analysis was used to translate predictive results into actionable retention insights.
