Geliştirilmiş RFM modeli ile müşteri segmentasyonu: Bir halı ve kilim üretici firmasında uygulama

Data science has gained enormous importance by contributing to the in-depth understanding and interpretation of information. Especially companies consult on data analysis to make strategic decisions in the competitive market. Much more important than the decisions taken is a determination of the customer or customer groups to which these decisions will be adapted. For that reason, customer segmentation by identifying similarities and differences between customers becomes crucial. In recent times, the RFM model is preferred mostly for customer segmentation. The RFM model is based on the customer's last purchase date, how often they purchase, and how much money contributes to the company. It is an easy model to understand and interpret results in a clear way. Many researchers prefer to apply the RFM method by adding extra variables to the analysis. Thus, customers are evaluated from a broader perspective. This study aims to present a developed RFM model by adding extra variables which are Loyalty, Dependence, and Expectation which are determined by a broad literature review and as a result of a survey relating to 106 dealers. There are some studies that create a segmentation model by using loyalty and the RFM model. However, this study developed a new model by including the dependence and expectation variables, which are not been used previously with the RFM model, besides loyalty. In the study, dealers are analyzed by the K-means clustering method and the optimum number of clusters is indicated as six. Each cluster has its specific customer behavior and this study guides the company to constitute marketing strategies regarding customers' specifications.