Enhancing Customer Churn Prediction and Retention for E-Commerce
DOI:
https://doi.org/10.15662/IJARCST.2026.0903003Keywords:
Customer Churn Prediction, Machine Learning, Data Preprocessing, Feature Engineering, Predictive Analytics, Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Model Evaluation, Accuracy, Precision, Recall, F1-Score, Customer Retention, Personalized Marketing, E-commerce Analytics, Data Visualization, Business Intelligence, Interactive DashboardAbstract
Customer churn is a major challenge in e-commerce, as losing customers directly impacts revenue and business growth. This project presents an intelligent customer churn prediction and retention system that utilizes machine learning techniques to identify customers who are likely to discontinue their engagement. The system is designed as a complete data pipeline, incorporating stages such as data collection, preprocessing, feature engineering, model training, and prediction. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting, are implemented to analyze customer behavior and improve prediction accuracy. The system evaluates and compares model performance using metrics such as accuracy, precision, recall, and F1-score to ensure optimal results. Based on the prediction outcomes, a retention strategy module is integrated to recommend business actions such as personalized offers, loyalty rewards, and targeted marketing campaigns to retain customers. Furthermore, the system includes visualization and reporting features through an interactive dashboard, enabling users to easily interpret insights and make data-driven decisions. This project demonstrates the effective application of machine learning in real-world e-commerce scenarios to enhance customer retention, optimize business strategies, and improve overall operational efficiency.
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