Segmentation Model of Customer Lifetime Value in Small and Medium Enterprise (SMEs) using K-Means Clustering and LRFM Model

Abstract

In this decade, SM Es have experienced substantial growth. According to the results of research conducted by the Retail Research Center, this sector experienced a growth rate of 18.6% in Europe in 2015 and 16.7% in 2016. The increasing co mpetition in the SM Es demanded this effort to improve techniques and strategies to maintain customer satisfaction levels to continue to increase .[1]. The SM Es sector has an important role in the country' s economy, especially Indonesia. They have proven their existence in the past few years. SM Es have a proportion of 99.99% of the total business actors in Indonesia or as many as 56.54 million units. Based on data achieved by SMEs, in 2013 the Abstract: The CLV model is a measure of customer profit fo r a co mpany that can be used to evaluate the future value of a customer. The CLV model is a measure of customer profit fo r a co mpany 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 clusters = 2 clusters. CLV values are generated fro m the mu ltip lication of the results of normalizat ion 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 h ighest from the other cluster average of 0.362. Based on LRFM matrix, this cluster has a high loyalty value with the symbol LRFM L ↑ R ↑ F ↑ M ↑ wh ich 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.

An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business

“…Wong and Wei [48] calculated the weighted RFM-Score. The weight determination for each variable of the RFM to create the RFMScore depends on the factor's importance in the application (Dursun and Caber [49]; Peker et al [50]); some authors applied the same weights to each attribute (Peker et al [50]; Hamdi and Zamiri [51]; Weng [52]), but other researchers applied the analytical hierarchical process (AHP) to define the correspondence weights, such as Moghaddam et al [25], He and Li [53], Rezaeinia and Rahmani [54], Marisa et al [55], Patel et al [56], Hosseini and Mohammadzadeh [57], Dachyar et al [58] and Monalisa et al [59]. We will take advantage of their findings and use AHP to define the different weights of each RFMScore per product category, obtaining a more complete approach to customer preferences and customer value.…”

A Combined Approach for Customer Profiling in Video on Demand Services Using Clustering and Association Rule Mining

“…Later studies also determined the length of customer involvement with the addition of the length (L) parameter [28]- [30], [31]. Identifying customer loyalty in this way and upon the length parameter has become more important today [32]. Other such parameters include the group (G) parameter to address product category information as well [33].…”

Intelligent Gamification Mechanics Using Fuzzy‐AHP and K‐Means to Provide Matched Partner Reference

“…CLV (Customer Lifetime Value) is an approach used to determine the level of customer loyalty to the company by testing the future value of customers to the company [4]. Meanwhile, K-Means clustering is a data mining approach to explore and group data according to the attributes selected for a specific purpose [4] , [5]. Data mining with K-Means was carried out based on the selected attributes, then weighted using AHP parameters to produce Customer Lifetime Value ranking analysis [6], [5].…”