Customer Segmentation Analysis Through RFM-D Model and K-Means Algorithm

Refri Martiansah · Siti Monalisa · Fitriani Muttakin · Mona Fronita

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Customer churn and segmentation Consumer Behavior and Marketing Influence Data Mining and Machine Learning Applications

Abstract

This research analyzes customer segmentation through the RFM-D (Recency, Frequency, Monetary, and Diversity) model and the K-Means algorithm. The data comes from sales transactions at Café Z from January 2023 to February 2024, with 10,212 entries. The applied methodology includes several stages: data pre-processing, cleaning, transformation, normalization, and clustering. Clustering validation was carried out using the Davies-Bouldin Index (DBI) to ensure the quality of the clusters formed. The analysis results identified three customer clusters based on purchasing behavior, indicating that the K-Means algorithm effectively groups customers. These findings provide insight for companies to design marketing strategies that are more focused and appropriate to the characteristics of each customer segment. Companies can improve operational efficiency, increase customer satisfaction, and maximize profitability by utilizing this segmentation. This research contributes to optimizing resource allocation and personalizing marketing approaches, ultimately strengthening customer relationships.

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2012Learning from television in young children 2020An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm 2020Customer Segmentation Based on RFM Model Using K-Means, K-Medoids, and DBSCAN Methods 2023New RFM-D classification model for improving customer analysis and response prediction 2021ANALISIS PEMILIHAN CLUSTER OPTIMAL DALAM SEGMENTASI PELANGGAN TOKO RETAIL 2021RFM Analysis Using K-Means Clustering to Improve Revenue and Customer Retention 2021Segmentasi Pelanggan Berdasarkan Analisis RFM Menggunakan Algoritma K-Means Sebagai Dasar Strategi Pemasaran (Studi Kasus PT Coversuper Indonesia Global)2020Segmenting Bank Customers via RFM Model and Unsupervised Machine Learning

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