Designing a Model Providing Services to Key Customers Based on RFM Model Using K-Means Clustering Method

As markets are becoming competitive and changing continuously, each customer has its own special value. Different organizations try to increase their competitive advantages by retaining and increasing customers’ loyalty. This research tries to design a model providing services to key customers based on R.F.M (Recency, Frequency, Monetary) model by using K_ means clustering method. The statistical population consists of two groups. The first group consists of ۱۸ experts from the Mellat Bank aimed at determining the weight of R, F, and M indicators. For clustering customers based on the RFM model, the second group constitutes the customers who have used POS device in ۲۰۱۷. To determine the weight of indicators, the Fuzzy Hierarchical Analysis Technique and Entropy Technique have been used. Also, K-Means method was used to analyze the data. According to the results, the weight of each one of the indicators of R, F, and M was obtained by using the hierarchical analysis process and entropy, and finally the weight of indicators was estimated as a combination. The weight of indicators was M = ۰.۵۹۹۸, F = ۰.۲۶۷۲ and R = ۰.۱۳۳۰, respectively. Furthermore, customers were clustered into six clusters. In the customers’ pyramid, the cluster number (۶) with the highest Customer Lifecycle Value (CLV) and ۷۸۳ customers has been the best cluster. Therefore, K-means method is a very suitable method for clustering customers and providing services to them, because after clustering and forming the customers’ pyramid it is easy to specify valuable and loyal customers of the bank.

Keywords:

Key Customers , RFM Model , Customer Lifecycle Value , K-Means Method