CUSTOMER SEGMENTATION BY USING RFM MODEL AND CLUSTERING METHODS: A CASE STUDY IN RETAIL INDUSTRY

Companies need to understand the customers' data better in all aspects. Detecting similarities and differences among customers, predicting their behaviors, proposing better options and opportunities to customers became very important for customer-company engagement. Segmenting the 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 widely 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 detected that the current customer segmentation which built by just considering customers' expense is not sufficient. Hence, models that recommended in this research are expected to provide better customer understanding, well-designed strategies, and more efficient decisions.