Segmentation model of customer lifetime value in Small and Medium Enterprise (SMEs) using k-means clustering and LRFM model

Fitri Marisa and Fachrudin and Syed Ahmad, Sharifah Sakinah and Mohd Yusoh, Zeratul Izzah and Aziz, Tubagus Mohammad Akhriza (2019) Segmentation model of customer lifetime value in Small and Medium Enterprise (SMEs) using k-means clustering and LRFM model. The International Journal of Integrated Engineering, 11 (3). pp. 169-180. ISSN 2600-7916

Text 2019 SEGMENTATION MODEL OF CUSTOMER LIFETIME VALUE IN SMALL AND MEDIUM ENTERPRISE (SMES) USING K-MEANS CLUSTERING AND LRFM MODEL.PDF Download (553kB)

Abstract

The CLV mode l is a measure of customer profit for a company that can be used to evaluate the future value of a customer. The CLV model is a measure of customer profit for a company that can be used to evaluate the future value of a customer. This study aims to obtain Customer Lifetime Value (CLV) in each customer segment. Grouping uses the K-Means Clustering method based on the LRFM model (Length, Recency, Frequency, Monetary). The cluster formation process uses the Elbow Method and SSE with the best number of c lusters = 2 clusters. CLV values are generated from the multiplication of the results of normalization of LRFM and the LFRM weight values are then summed, and carried out on each cluster that has been formed. The highest ranking among the 2 clusters is at the second cluster with the CLV value being far the highest from the other c luster average of 0.362. Based on LRFM matrix, this cluster has a high loyalty value with the symbol LRFM L ↑ R ↑F ↑ M ↑ which is a loyal customer (the best segment that has high customer loyalty value).Based on the LRFM symbol, the company can make a strategy to retain customers and acquire customers to become loyal customers with high profitability.

Item Type:	Article
Uncontrolled Keywords:	CLV, LRFM, K-Means, Clustering, SMEs
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	08 Dec 2020 15:04
Last Modified:	21 Sep 2026 05:32
URI:	http://eprints.utem.edu.my/id/eprint/24633
Statistic Details:	View Download Statistic

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