K-Means Kümeleme Algoritması ve RFM Modeli Kullanarak Müşteri Segmentasyonu

Abstract

Hedef müşterinin belirlenmesi ve ihtiyaçlarının karşılanması, müşteri segmentasyonunda önemli noktalardır. Yenilik-Sıklık-Tutar (RFM) Analizi ve K-Means kümeleme algoritması, müşteri davranışını analiz eden müşteri segmentasyonu için kullanılan popüler yöntemlerdir. Çalışmamızda, ev cihazlarının, RFM bileşenlerini temsil edecek şekilde özelliklerini çıkararak K-Means kümeleme algoritmasını RFM modeline uyarladık. Böylece, benzer RFM özelliklerine sahip müşteriler aynı kümelere atanırken, benzer olmayan RFM özelliklerine sahip müşteriler farklı kümelere atanmıştır. Deneylerde, kümeleme çalışmasının, belirlenen Silhouette Skorunu geçerek başarılı olduğu gözlenmiştir. Ortaya çıkan kümeler, bir müşterinin işletme için ne kadar değerli olduğunu ölçen Müşteri Yaşam Boyu Değeri (CLV) metriğine göre sıralanmış ve adlandırılmıştır.

Keywords

• Müşteri Segmentasyonu
• RFM Model
• K-Means Algoritması
• Müşteri Yaşam Boyu Değeri

Customer Segmentation Using K-Means Clustering Algorithm and RFM Model

Abstract

The key points in customer segmentation are determining target customer groups and satisfying their needs. Recency-Frequency-Monetary (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 (CLV) metric, which measures how valuable a customer is to the business.

Keywords

• Customer Segmentation
• RFM Model
• K-Means Algorithm
• Customer Lifetime Value

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