Book

Description

Intelligent Customer Churn Prediction in E-Business: Models, Methods, and Applications In today's competitive digital economy, retaining customers is as critical as acquiring them. Customer churn presents a significant challenge for e-commerce platforms, subscription-based services, SaaS applications, and digital businesses, directly affecting profitability and long-term sustainability. Intelligent Customer Churn Prediction in E-Business explores advanced machine learning techniques for identifying customers at risk of leaving and enabling proactive retention strategies. The book presents an integrated predictive framework built around Extreme Gradient Boosting (XGBoost), combining data preprocessing, feature engineering, class imbalance management, and explainable artificial intelligence. Through comparative analysis with established machine learning models and the application of SHAP-based explainability, the book demonstrates how predictive accuracy and model transparency can support informed business decisions. Bridging academic research and practical applications, it offers valuable insights for researchers, data scientists, business analysts, and digital enterprises seeking to strengthen customer relationships, improve retention, and achieve sustainable growth.