Implementasi Model LRFM dan Algoritma K-Means dalam Segmentasi Pelanggan Usaha Aluminium dan Kaca

Abstract

This research implements the LRFM model and K-Means algorithm to find customer segments and analyze each segment. The LRFM model determines customer purchasing behavior using 4 variables (Length, Recency, Frequency, and Monetary) processed from sales and customer data of Mulia Jasa Aluminum and Glass Business. The LRFM model will be grouped using the K-Means algorithm and the Silhouette method to determine the segment of customer purchasing behavior. This research adapts the CRISP-DM (Cross Industry Standard Process for Data Mining) framework for data analysis. This research aims to help Mulia Jasa business find its customer groups as a consideration for making more targeted decisions in determining marketing strategies and services to customers. The results of this study are 3 customer clusters including cluster 0 with total data of 132 customers, cluster 1 with total data of 144 customers, and cluster 2 with total data of 28 customers.