Customer Segmentation Using K-Means Clustering Algorithm and RFM Model

Authors : Gözde ASLANTAŞ, Mustafacan GENÇGÜL, Merve RUMELLİ, Mustafa ÖZSARAÇ, Gözde BAKIRLI

Pages : 491-503

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Publication Date : 2023-05-15

Article Type : Research Paper

Abstract :The key points in customer segmentation are determining target customer groups and satisfying their needs. Recency-Frequency-Monetary insert ignore into journalissuearticles values(RFM); analysis and K-Means clustering algorithm are the popular methods for customer segmentation when analyzing customer behavior. In our study, we adapt the K-means clustering algorithm to RFM model by extracting features that represent RFM aspects of home appliances. Customers with similar RFM-oriented features are assigned to the same clusters, while customers with non-similar RFM-oriented features are assigned to different clusters. In the experiments, clustering achieved the determined threshold for Silhouette Score. The resulting clusters were ranked and named by Customer Lifetime Value insert ignore into journalissuearticles values(CLV); metric, which measures how valuable a customer is to the business.
Keywords : Müşteri Segmentasyonu, RFM Model, K Means Algoritması, Müşteri Yaşam Boyu Değeri