J-WS: A Hybrid Unsupervised Mining Approach for Customer Segmentation in B2C e-Commerce

DOI

10.2991/978-94-6463-094-7_13How to use a DOI?

Keywords

Association rule mining; Customer segmentation; E-commerce; Hierarchical; K-Means; RFM model

Abstract

Nowadays, with the use of technology and the Internet, it is easy to start a business, more specifically an e-commerce business. However, maintaining a consistent sale and having returning customers can prove a challenge as most businesses rely on new customers for profits and does not generate a reliable profit as compared to relying on old customers. One might resort to applying different kinds of marketing strategies but without understanding of their customer base and proper segmentation of customers, these efforts could result in waste of resources and low probability of success. Therefore, an approach named J-WS that can perform customer segmentation based on customer sales data and Recency, Frequency, and Monetary (RFM) model is proposed. Meaningful information from different groups of customers can later be utilized by target marketing strategy to improve customer retention and impactful marketing. The proposed work consists of 5 phases which include data cleaning, identifying the best clustering algorithm between K-Means and Hierarchical clustering in terms of execution time and Sum of Squared Error, applying association rule mining to generate sets of frequent association rules among the clusters. Conclusively, J-WS can be used by e-commerce businesses to segment their customers meaningfully and properly.

Copyright

© 2022 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  - Muhammad Azry Bin Ali
AU  - Fang-Fang Chua
AU  - Amy Hui Lan Lim
PY  - 2022
DA  - 2022/12/27
TI  - J-WS: A Hybrid Unsupervised Mining Approach for Customer Segmentation in B2C e-Commerce
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 156
EP  - 170
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_13
DO  - 10.2991/978-94-6463-094-7_13
ID  - Ali2022
ER  -