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Machine Learning-Based Prediction of Customer Churn Risk in E-commerce
- 1 School of International Business of SWUFE, Southwestern University of Finance and Economics, Chengdu City, Sichuan Province, 610000, China
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Abstract
Amidst the booming development of e-commerce and intense market competition, numerous e-commerce companies frequently encounter the issue of customer loss. This research endeavors to offer a comprehensive analysis and precise forecasting of customer churn behavior for an E-commerce company. The research utilizes the “E-commerce Customer Churn” dataset From Kaggle, which offers a wealth of customer information. The paper initially performs a data cleaning to fill the missing value by K-nearest neighbors (KNN). And then, it also performs feature engineering to preprocess the dataset. Subsequently, multiple machine learning models were constructed, including Logistical Regression (LR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Neural Network (NN), and a stacking model with a metal-leaner as Extreme Gradient Boosting (XGBoost) has been developed. The stacking model achieved the highest performance with 92.8% accuracy and 0.940 AUC. Key factors such as tenure, complaints, cashback amount, order recency, and satisfaction score were identified as important predictors. This research demonstrates the potential of Machine Learning in developing effective retention strategies for e-commerce platforms.
Keywords
Customer Churn Risk in E-commerce, Logistical Regression (LR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Neural Network (NN)
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Cite this article
Ren,H. (2025). Machine Learning-Based Prediction of Customer Churn Risk in E-commerce. Advances in Economics, Management and Political Sciences,153,47-52.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis
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