The new approach in market segmentation by using RFM model

Data analytics allows companies mining the patterns and trends in their customers’ data to implement more effective market segmentation strategies, then customize promotional offers, allocate marketing resources efficiently, and improve Customer Relationship Management (CRM). However, the implementation of such strategies often hampered by limited budgets and the ever-changing priorities and goals of marketing campaigns. So, this paper suggests and demonstrates the novel approach dividing a broad target market into subsets of consumers who have common needs, interests, and priorities, and then designing and implementing strategies to target them to achieve profit maximization. Therefore, the aims of this study are twofold; First, is to use historical data (such as purchased items and the associative monetary expenses), the proposed model identifies customer segments based on Firefly Algorithm (FA), and Second, is the identification of the most profitable segment according to the RFM model (recency, frequency and monetary). The data were obtained from the sales department of one of the branch stores in northern region of Iran. There were 3836 transactions generated jointly by 211 customers in a transaction database containing 30 items. Findings revealed that there are three optimize clusters for this store and cluster number 3 is the most profitable.