Article: Customer churn prediction in e-commerce platforms using multi-feature fusion Journal: International Journal of Computer Applications in Technology (IJCAT) 2026 Vol.78 No.6 pp.29 - 39 Abstract: To improve the accuracy of customer churn prediction and ensure the quality of e-commerce platform services, a multi-feature fusion-based e-commerce platform customer churn prediction method is proposed. Firstly, the SMOTE algorithm is used to reduce data imbalance and construct a data set for predicting customer churn on e-commerce platforms; Then, select user characteristic attributes, analyse customer consumption behaviour based on consumption time characteristics, consumption value characteristics and consumption quantity characteristics, and integrate multiple characteristics of consumption behaviour; Finally, use the blending model and four base learners RF, GBTD, XGBoost and LightGBM, along with the secondary learner of the logistic regression model, to achieve customer churn prediction on e-commerce platforms. Experimental results have shown that the maximum error in predicting customer churn rate using the method proposed in this article does not exceed 0.1%, with an AUC value of 0.913 and a maximum accuracy of 0.92. Inderscience Publishers - linking academia, business and industry through research

Title: Customer churn prediction in e-commerce platforms using multi-feature fusion

Authors: Ming Yang

Addresses: Business School, Chongqing Polytechnic University of Electronic Technology, Chongqing, China

Abstract: To improve the accuracy of customer churn prediction and ensure the quality of e-commerce platform services, a multi-feature fusion-based e-commerce platform customer churn prediction method is proposed. Firstly, the SMOTE algorithm is used to reduce data imbalance and construct a data set for predicting customer churn on e-commerce platforms; Then, select user characteristic attributes, analyse customer consumption behaviour based on consumption time characteristics, consumption value characteristics and consumption quantity characteristics, and integrate multiple characteristics of consumption behaviour; Finally, use the blending model and four base learners RF, GBTD, XGBoost and LightGBM, along with the secondary learner of the logistic regression model, to achieve customer churn prediction on e-commerce platforms. Experimental results have shown that the maximum error in predicting customer churn rate using the method proposed in this article does not exceed 0.1%, with an AUC value of 0.913 and a maximum accuracy of 0.92.

Keywords: e-commerce platform; customer churn; multi-feature fusion; blending model; learner.

DOI: 10.1504/IJCAT.2026.153743

International Journal of Computer Applications in Technology, 2026 Vol.78 No.6, pp.29 - 39

Received: 26 Aug 2025
Accepted: 09 Dec 2025
Published online: 22 May 2026 *

Title: Customer churn prediction in e-commerce platforms using multi-feature fusion

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