E-commerce Customer Segmentation Using K-Means and Hierarchical Clustering

DOI

10.2991/978-94-6239-801-6_150How to use a DOI?

Keywords

Customer Segmentation; K-Means Clustering; Hierarchical Clustering; E-commerce; Retail Data

Abstract

With the rapid growth of online shopping, e-commerce businesses face intense competition and need better ways to understand consumers. Customer segmentation has become an essential part of business analytics, helping companies recognise different customer groups and design personalised marketing plans. Therefore, it is meaningful to explore simple and effective clustering methods for retail customer data. This study primarily compares two common unsupervised learning algorithms: K-Means and hierarchical clustering. Using the public Mall Customer Dataset, the research conducted data cleaning, feature encoding, and standardisation before clustering. The number of clusters is set to five for fair comparison. Silhouette score, Calinski–Harabasz score, and Davies–Bouldin index are used to evaluate clustering quality. The results show that K-Means produces more compact, value-based groups and performs better in most metrics. Hierarchical clustering is easier to interpret in demographic terms but less useful for value analysis. Overall, K-Means is more practical for e-commerce customer segmentation, while hierarchical clustering can serve as a supplementary exploratory tool.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuyuan Feng
PY  - 2026
DA  - 2026/10/23
TI  - E-commerce Customer Segmentation Using K-Means and Hierarchical Clustering
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 1431
EP  - 1438
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_150
DO  - 10.2991/978-94-6239-801-6_150
ID  - Feng2026
ER  -