Article: Predicting e-commerce customer churn using a PCA-AdaBoost combined model Journal: International Journal of Web and Grid Services (IJWGS) 2025 Vol.21 No.3/4 pp.269 - 289 Abstract: Customer loyalty is closely related to the development of e-commerce platforms, but due to the non-contractual characteristics of e-commerce users and the poor performance of traditional customer data analysis, the phenomenon of customer churn is more prominent and obvious. Therefore, based on this, a combination prediction model is proposed to analyse customer data, which optimises indicator data on the basis of recency, frequency, and monetary models. By adding emotional feature indicators, customer rating indicators, and introducing an improved K-value clustering algorithm, the problem of customer churn prediction is analysed. Subsequently, principal component analysis is used to reduce the dimensionality of the data and combined with adaptive boosting algorithms to better ensure classification accuracy. The results show that the overall accuracy of the combined algorithm on the dataset is above 98%, significantly superior to other algorithms, and its recall results for non-churn customers are also above 97%, with a mean absolute percentage error of less than 2%. The specific stability and fitting are good, with the overall accuracy value and consistency coefficient basically below 0.02. This combination prediction model can effectively provide reference value for e-commerce operators to improve customer relationship management and reduce customer churn issues. Inderscience Publishers - linking academia, business and industry through research

Title: Predicting e-commerce customer churn using a PCA-AdaBoost combined model

Authors: Yang Ding

Addresses: School of Economics and Management, Xi'an University of Technology, Xi'an, 710054, China; School of Accounting and Finance, Xi'an Peihua University, Xi'an, 710125, China

Abstract: Customer loyalty is closely related to the development of e-commerce platforms, but due to the non-contractual characteristics of e-commerce users and the poor performance of traditional customer data analysis, the phenomenon of customer churn is more prominent and obvious. Therefore, based on this, a combination prediction model is proposed to analyse customer data, which optimises indicator data on the basis of recency, frequency, and monetary models. By adding emotional feature indicators, customer rating indicators, and introducing an improved K-value clustering algorithm, the problem of customer churn prediction is analysed. Subsequently, principal component analysis is used to reduce the dimensionality of the data and combined with adaptive boosting algorithms to better ensure classification accuracy. The results show that the overall accuracy of the combined algorithm on the dataset is above 98%, significantly superior to other algorithms, and its recall results for non-churn customers are also above 97%, with a mean absolute percentage error of less than 2%. The specific stability and fitting are good, with the overall accuracy value and consistency coefficient basically below 0.02. This combination prediction model can effectively provide reference value for e-commerce operators to improve customer relationship management and reduce customer churn issues.

Keywords: e-commerce; RFM model; PCA; AdaBoost algorithm; customer churn; self organising mapping; SOM; K-means clustering.

DOI: 10.1504/IJWGS.2025.150174

International Journal of Web and Grid Services, 2025 Vol.21 No.3/4, pp.269 - 289

Received: 19 Sep 2024
Accepted: 06 Aug 2025
Published online: 02 Dec 2025 *

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Title: Predicting e-commerce customer churn using a PCA-AdaBoost combined model

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