Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65503
Volume:
10 |
6 |
Jun 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science
JSPM University,Wagholi, Pune
Department of Computer Science
JSPM University,Wagholi, Pune
The rapid advancement of e-commerce websites has produced a new level of customer information from which it is difficult for companies to take an accurate perspective of customer activity and provide tailored customer service. Traditional marketing techniques are typically used to promote product/service offerings using the same marketing strategy to all customers. This approach often results in decreased customer engagement with companies and marketing methods. This research focuses upon the application of customer segmentation through the use of data mining methodologies; with the emphasis of this research being upon customer demographics and purchasing behaviours, in order to facilitate the creation of useful customer groups and ultimately improve upon business decision making. The segmentation methodology proposed by this research is the K-means clustering algorithm, which is a data mining technique that is easy to understand and provides a means for businesses to improve their understanding of customer behaviour, create targeted marketing programmes, enhance customer satisfaction and assist with data-driven decision making. This research also provides an overview of the various customer segmentation methodologies available today and explains their significance to the modern day e-commerce system.
KEYWORDS
Customer segmentation has become an essential aspect of modern e-commerce systems. The current study has looked at various data mining methods and implemented K-Means clustering as the main strategy of segmentation. The data shows that by segmenting customers, a business can have a better understanding of its customers, develop personalised marketing strategies, and improve its overall performance. As eCommerce platforms continue to develop, data-driven customer analysis will continue to be a critical element for future business growth [5].
[1] C.-G. Wong, G.-K. Tong, and S.-C. Haw, "Exploring Customer Segmentation in E-Commerce using RFM Analysis with Clustering Techniques," Journal of Telecommunications and the Digital Economy, vol. 12, no. 3, pp. 97–125, Sep. 2024, doi: 10.18080/jtde.v12n3.978. [2] R. Siagian, P. Sirait, and A. Halima, "E-Commerce Customer Segmentation Using K-Means Algorithm and Length, Recency, Frequency, Monetary Model," Journal of Informatics and Telecommunication Engineering, vol. 5, no. 1, pp. 21–30, Jul. 2021, doi: 10.31289/jite.v5i1.5182. [3] P. Rajapandian, A. Karunamurthy, V. Vasanth, and M. Meganathan, "E-Commerce Customer Segmentation: A Clustering Approach in a Web-Based Platform," Journal of Engineering Technology and Applied Physics, vol. 7, no. 1, pp. 71–79, Mar. 2025, doi: 10.33093/jetap.2025.7.1.12. [4] N. Nehemia, J. N. Pakage, V. Lois, and R. Arieskha, "E-Commerce Customer Segmentation Application Based on the K-Means Algorithm," Journal of Artificial Intelligence and Engineering Applications, vol. 5, no. 2, 2025, doi: 10.59934/jaiea.v5i2.1922. [5] S. Wang, L. Sun, and Y. Yu, "A Dynamic Customer Segmentation Approach by Combining LRFMS and Multivariate Time Series Clustering," Scientific Reports, vol. 14, Article no. 17491, 2024.
Publication Date
6/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
4
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...