An Enhanced RFM Customer Value-Based Customer Segmentation and Evaluation

Title

An Enhanced RFM Customer Value-Based Customer Segmentation and Evaluation

Creator

Vijayalakshmi, S.; Gayathri, S.P.

Description

Machine Learning Algorithms are widely used in the contemporary era of highly compatible technical improvements to provide answers to the challenges of business environment, yet crucial services for a firm to run successfully in this intensely competitive E-commerce sector. Recently, strategies like clustering and classification mechanisms that allow for the classification of both existing and new clients into clusters have also produced positive outcomes. Recency, Frequency, and Monetary (RFM) measures are hugely being used these days to perform these kinds of tasks. In this study, individual one-dimensional clustering on the Recency, Frequency, and Monetary columns was performed, and a weighted average or preferred linear combination of the three features was then used to calculate an overall score. Summing up the result of three individual clusters. Finally, all of the distinct clients were divided into these three segments based on the overall score, which was divided into three categories. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Networks and Systems;Volume;1362 LNNS;pp.511-523

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Classification; Clustering; Machine learning; RFM

Coverage

Vijayalakshmi S., CHRIST University, Bengaluru, India; Gayathri S.P., Government Arts College for Women, Tamil Nadu, Nilakottai, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981965213-6;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-5214-3_41

https://www.scopus.com/pages/publications/105015664535?origin=resultslist