A Case Study of Applying Customer Segmentation in A Medical Equipment Industry

Abstract

The purpose of this paper is to apply LRFM (length, recency, frequency, monetary) for customers in the medical equipment industry and identify differences in each customer segment. This study uses LRFM and clustering to segment its customers. This research uses transaction data of the medical device industry in Indonesia. This data will be extracted for the length, recency, frequency, and monetary (LRFM). The optimal cluster obtained from the validation process is four which will be used as a basis for customer segmentation. This study uses the K-Means algorithm as a clustering method and Decision Tree as a classification method and the application of IF-THEN rules. The segmentation process will be identified based on LRFM criteria in each segment that has been formed and will form a marketing strategy that is appropriate for the company. The results obtained from this study are four customer segments based on LRFM with each segment given a profile name as: Best, Frequent, Low and Uncertain. This study provides guidance on customer identification based on LRFM that can be used by medical equipment companies to develop strategies that are in accordance with the criteria of each segment that has been obtained to improve customer relationships management system and new ways of marketing products.