B2C E-Commerce Customer Churn Prediction Based on K-Means and SVM

Xiancheng Xiahou · Yoshio Harada

149 citations0/0 files storedJournal of theoretical and applied electronic commerce research

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Customer churn prediction is very important for e-commerce enterprises to formulate effective customer retention measures and implement successful marketing strategies. According to the characteristics of longitudinal timelines and multidimensional data variables of B2C e-commerce customers’ shopping behaviors, this paper proposes a loss prediction model based on the combination of k-means customer segmentation and support vector machine (SVM) prediction. The method divides customers into three categories and determines the core customer groups. The support vector machine and logistic regression were compared to predict customer churn. The results show that each prediction index after customer segmentation was significantly improved, which proves that k-means clustering segmentation is necessary. The accuracy of the SVM prediction was higher than that of the logistic regression prediction. These research results have significance for customer relationship management of B2C e-commerce enterprises.

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References

1995Why Satisfied Customers Defect 1996Why Satisfied Customers Defect 2006Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models 2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2004Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting 2016Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction

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2023Customer Churn Prediction in the Banking Sector Using Machine Learning-Based Classification Models 2024Customer churn prediction model based on hybrid neural networks 2025Enhancing customer retention in Online Retail through churn prediction: A hybrid RFM, K-means, and deep neural network approach 2022Analysis of Unsupervised Machine Learning Techniques for an Efficient Customer Segmentation using Clustering Ensemble and Spectral Clustering

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