An Integrated Machine Learning Model for E-Commerce Churn Prediction

Customer churn is a major challenge in the e-commerce industry, where customers end their relationship with an online business due to reasons like dissatisfaction with product quality, poor customer service, pricing concerns, fierce competition, or changing preferences. This study introduces an integrated machine learning approach to predict customer churn in e-commerce, combining k-means clustering for customer segmentation and XGBoost for classification within the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The model aims to deliver a comprehensive, stable, and reliable churn-prediction solution by analyzing customer data such as purchase history and demographics. The methodology ensures a thorough and insightful analysis of customer data to improve prediction accuracy. The model achieved an accuracy of 98.68%, precision of 96.19%, recall of 94.39%, and F1 score of 95%, outperforming individual algorithms used in earlier or similar studies. These results demonstrate the effectiveness of the integrated approach in predicting customer churn and offer valuable insights for e-commerce businesses, highlighting the importance of using advanced machine-learning techniques to boost customer retention and profitability. The study adds to the less-explored area of churn prediction in e-commerce and shows the potential of combined machine learning approaches to solve this critical issue.