CUSTOMER SEGMENTATION BY USING RFM MODEL AND CLUSTERING METHODS: A CASE STUDY IN RETAIL INDUSTRY - Doğan, Onur; Ayçin, Ejder; Bulut, Zeki Atıl (International Journal of Contemporary Economics and Administrative Sciences, 2018)
In today’s business environment companies should need better understanding on customers’ data. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities to customers, etc. became very important for customer-company engagement. Segmenting customers according to their data became vital in this context. RFM (recency, frequency and monetary) values have been used for many years to identify which customers valuable for the company, which customers need promotional activities, etc. Data-mining tools and techniques commonly have been used by organizations and individuals to analysis their stored data. Clustering, which one of the tasks of data mining has been used to group people, objects, etc. In this paper we propose two different clustering models to segment 700032 customers by considering their RFM values. We suggest that the current customer segmentation which built by just considering customers’ expense is not sufficient. Hence, one of the models that recommended in this research is expected to provide better customer understanding, well-designed strategies, and more efficient decisions.