Industrial Engineering Department K.N.Toosi University of Technology Tehran, Iran.
Information Technology Department, Electronic Branch, Islamic Azad University, Tehran, Iran.
Citation: Sahar Ghoreishi, Keyvan Khandestani, “Customer Segmentation Based on GRFM: Case Study”, The World of Computer Science and Information Technology Journal (WSCIT). 2019 Volume 9, Issue 1, pp.1.6.
Abstract—in the last decades’ firms which have directly or indirectly contact with a customer migrate from product-oriented to be a customer-oriented, hence, some products and customers are not profitable in the same way and some of them bring detriment to the firm. In this regard, firms should recognize loyal, profitable and potential customers with a glance of impressive product which brings added value for them. In order to distinguish profitable customers, they supposed to cluster customers and study their behavior’s group for the sake of having the best investment in the best segment. In this paper, we utilize customize GRFM (Group RFM) to cluster customers based on proposed APC (account-pattern constraint clustering) algorithm. Hence, we calculate the cluster RFM value which could aid the bank to explore both profitable accounts and customers.
Keywords-Component; Data Mining; Constraint Clustering Algorithms; Segmentation; RFM.