A Comparative Analysis of RFM-based Customer Segmentation with K-Means and BIRCH Clustering Techniques

DOI

10.2991/978-94-6463-471-6_94How to use a DOI?

Keywords

Customer Segmentation; E-Commerce; Machine Learning; K-Means; BIRCH; RFM Model

Abstract

Marketing is an expensive activity in the realm of product sales. In today's world, most businesses have a lot of digital data that involves consumer transaction records. Segmenting clients into various prominent groupings and designing personalized activities for each cluster is a critical technique for determining such effective marketing tactics. Techniques for obtaining relevant insights from digital data have progressed significantly over time. Importantly, machine learning, a process that allows computers to gain insight and interpret data, has piqued the interest of researchers. This paper illustrates the implementation of the marketing technique called RFM model along with the k-means and BIRCH machine learning clustering algorithms on the e-commerce customers sales dataset resulting in fruitful customer segmentation. A comparative analysis is also performed which resulted in k-means outperforming the BIRCH.

Copyright

© 2024 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  - M. V. Rajesh
AU  - S. Rao Chintalapudi
AU  - M. H. M. Krishna Prasad
PY  - 2024
DA  - 2024/07/30
TI  - A Comparative Analysis of RFM-based Customer Segmentation with K-Means and BIRCH Clustering Techniques
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 977
EP  - 989
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_94
DO  - 10.2991/978-94-6463-471-6_94
ID  - Rajesh2024
ER  -