Segmentasi Konsumen Menggunakan Algoritma K-Means Clustering Dan Analisis Rfm Guna Mengetahui Karakteristik Konsumen

Abstract

This research aims to segment consumers at MDA Collection stores using the K-Means Clustering algorithm and RFM (Recency, Frequency, Monetary) analysis, with the aim of understanding consumer purchasing characteristics and supporting the development of more effective marketing strategies at MDA Collection stores. The research shows that the K-Means Clustering algorithm is successful in grouping MDA Collection consumers into homogeneous segments based on their purchasing data. Analysis of the characteristics of each segment helps in understanding consumer preferences and purchasing habits. By better understanding consumer characteristics, MDA Collection Stores can design more effective marketing strategies to maintain consumer loyalty. The results of this research show that there are 4 consumer segments with different characteristics. In segment 1 there are 24 consumers who are included in the loyal consumer profile, in segment 2 there are 126 consumers who are included in the less loyal consumer profile, in segment 3 there are 83 consumers who are included in the less loyal consumer profile, and in segment 4 there are 115 consumers who are included in the potential consumer profile.