CUSTOMER SEGMENTATION WITH CLUSTERING METHOD USING RFM METRICS: AN APPLICATION IN RETAIL INDUSTRY

Abstract

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.