E-commerce Customer Segmentation Using Machine Learning

Yuqin Yang

doi:10.54254/2754-1169/2025.BL29390

Advances in Economics, Management and Political SciencesOpen access

E-commerce Customer Segmentation Using Machine Learning

Research Article

Open Access

E-commerce Customer Segmentation Using Machine Learning

Yuqin Yang ^1*

¹ Hunan International Economics University

^*Corresponding author: BeckyYyq1997@outlook.com

Published on 11 November 2025

AEMPS Vol.240

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-527-1

ISBN (Online): 978-1-80590-528-8

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Abstract

E-commerce has increased exponentially over the past few years, with opportunities for growth as well as daunting tasks for businesses selling in competitive online spaces. One of the persisting challenges is how to identify and retain valuable customers whose purchasing behavior changes quickly in response to promotions, new product launches, or social media. This study responds by developing a hybrid segmentation model that incorporates Recency-Frequency-Monetary (RFM) measures with the K-Means clustering algorithm. Using transactional-level data, it construct behavioral features and apply clustering to detect patterns not normally captured by static thresholds. Four segments are revealed through analysis: high-value loyal purchasers, mid-value customers with growth potential, disengaged segments at risk for churn, and a small premium spending segment. RFM affords interpretability, and K-Means detects latent structure that yields analytical insight. Overall, the findings provide managers with concrete recommendations for loyalty programs, reactivation campaigns, and premium services, showcasing how machine learning can complement the role of traditional metrics in e-commerce.

Keywords:

E-commerce, customer, machine learning

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References

[1]. Wong, K., Chan, H., Lee, M. and Ho, J. (2024) Exploring Customer Segmentation in E-Commerce Using RFM Analysis with Hierarchical Clustering. Journal of Telecommunications and the Digital Economy, 12, 45–62.

[2]. Zhao, Y. and Balagué, C. (2023) Customer Lifetime Value Prediction in E-Commerce: A Deep Learning Approach. Journal of Business Research, 154, 113–125.

[3]. Haddadi, A., Rahimi, M., Shafiei, S. and Karami, A. (2025) A Hybrid Model for Improving Customer Lifetime Value. Information Sciences, 678, 119-134.

[4]. Sun, Y., Li, H., and Wang, J. (2024) A Dynamic Customer Segmentation Approach by Combining LRFMS and Time-Series Clustering. Expert Systems with Applications, 241, 12-23.

[5]. Zhang, R., Liu, P., and Chen, Q. (2025) Customer Segmentation Using RFM and K-Means Clustering to Support CRM in Retail Industry. Decision Support Systems, 172, 114-151.

[6]. Vo, N., Pham, T., and Nguyen, L. (2025) Automatic K-Optimization and RFM-Based K-Means for Customer Segmentation. ACM Transactions on Management Information Systems, 13, 67–85.

[7]. Wang, L., Zhou, Y., and Fang, M. (2025) Data-driven Strategic Customer Segmentation Considering Behavioral Dynamics. Information Sciences, 701, 120-122.

[8]. Liu, J., Wang, R., and Zhang, X. (2025) Unveiling Consumer Patterns with Machine Learning: From RFM to Deep Clustering. Applied Intelligence, 63, 1123–1142.

References

[2]. Zhao, Y. and Balagué, C. (2023) Customer Lifetime Value Prediction in E-Commerce: A Deep Learning Approach. Journal of Business Research, 154, 113–125.

[3]. Haddadi, A., Rahimi, M., Shafiei, S. and Karami, A. (2025) A Hybrid Model for Improving Customer Lifetime Value. Information Sciences, 678, 119-134.

[4]. Sun, Y., Li, H., and Wang, J. (2024) A Dynamic Customer Segmentation Approach by Combining LRFMS and Time-Series Clustering. Expert Systems with Applications, 241, 12-23.

[5]. Zhang, R., Liu, P., and Chen, Q. (2025) Customer Segmentation Using RFM and K-Means Clustering to Support CRM in Retail Industry. Decision Support Systems, 172, 114-151.

[6]. Vo, N., Pham, T., and Nguyen, L. (2025) Automatic K-Optimization and RFM-Based K-Means for Customer Segmentation. ACM Transactions on Management Information Systems, 13, 67–85.

[7]. Wang, L., Zhou, Y., and Fang, M. (2025) Data-driven Strategic Customer Segmentation Considering Behavioral Dynamics. Information Sciences, 701, 120-122.

[8]. Liu, J., Wang, R., and Zhang, X. (2025) Unveiling Consumer Patterns with Machine Learning: From RFM to Deep Clustering. Applied Intelligence, 63, 1123–1142.

Cite this article

Yang,Y. (2025). E-commerce Customer Segmentation Using Machine Learning. Advances in Economics, Management and Political Sciences,240,102-109.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICFTBA 2025 Symposium: Data-Driven Decision Making in Business and Economics

ISBN: 978-1-80590-527-1(Print) / 978-1-80590-528-8(Online)

Editor: Lukášak Varti

Conference website: https://2025.icftba.org/Bratislava.html

Conference date: 12 December 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.240

ISSN: 2754-1169(Print) / 2754-1177(Online)

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