RFM Metriklerini Kullanarak Kümeleme Yöntemi ile Müşteri Bölümlendirme: Perakende Sektöründe Bir Uygulama

Dünyadaki makro faktörlerin değişimine ve gelişimine paralel olarak pazarlamada da kitlesel pazarlama anlayışından müşteri anlayışına bir geçiş süreci yaşanmıştır. Kuşkusuz bu süreçte en önemli konulardan birisi de müşteri bölümlendirmedir. Çünkü müşteri bölümlendirmenin müşteri odaklı pazarlama anlayışında müşterilerin çeşitli parametreler çerçevesinde sınıflandırılması ve kümelenmesinde önemli bir fonksiyon üstlenebilmektedir. Literatürde de uygulamadaki gelişmelere paralel olarak bu konuya ilginin arttığı ve çalışmaların zenginleştiği gözlemlenmektedir. Ancak halen bu konuda yapılan çalışmaların sınırlı olduğu anlaşılmaktadır. Bu noktadan hareketle bu çalışmada RFM metrikleri temelinde müşterilerin kümelere ayrılması ve pazarlama bakış açısıyla yorumlanması amaçlanmıştır. Dolaysıyla bu amacı gerçekleştirmek için Karaman bölgesinde faaliyet gösteren bir gıda perakende işletmesinden veriler elde edilmiştir. Bu verilerin analizinde RFM ve k-ortalama teknikleri kullanılmıştır. Yapılan analiz sonucu toplamda 6 müşteri kümesi oluşmuştur. En karlı müşteri 3 numaralı müşteri kümesi iken, en karsız ve neredeyse terk etmek üzere olan müşteri kitlesi 1 ve 6 numaralı müşteri kümeleri olmuştur.

Customer Segmentation with Clustering Method Using RFM Metrics: An Application in Retail Industry

In parallel with the change and development of macro factors in the world, a transition period from mass marketing understanding to customer-oriented understanding has been experienced in marketing. Undoubtedly, one of the most important issues in this process is customer segmentation. Because of the customer-oriented marketing approach of customer segmentation, it can play an important function in the classification and clustering of customers within the framework of various parameters. In the literature, parallel to the developments in practice, it is observed that the interest in this subject has increased and the studies have been enriched. However, it is understood that the studies on this subject are still limited. From this point of view, this study aims to divide customers into clusters on the basis of RFM metrics and to interpret them from a marketing perspective. Therefore, data was obtained from a food retail business operating in the Karaman region to achieve this goal. RFM and k-mean techniques were used in the analysis of these data. As a result of the analysis, a total of 6 customer clusters were formed. While the most profitable customer was the number 3 customer cluster, the most unprofitable and almost abandoned customer clusters were the number 1 and 6 customer clusters.

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