Measuring customer loyalty using an extended RFM and clustering technique

Today, the ability to identify the profitable customers, creating a long-term loyalty in them and expanding the existing relationships are considered as the key and competitive factors for a customer-oriented organization. The prerequisite for having such competitive factors is the presence of a very powerful customer relationship management (CRM). The accurate evaluation of customers’ profitability is considered as one of the fundamental reasons that lead to a successful customer relationship management. RFM is a method that scrutinizes three properties, namely recency, frequency and monetary for each customer and scores customers based on these properties. In this paper, a method is introduced that obtains the behavioral traits of customers using the extended RFM approach and having the information related to the customers of an organization; it then classifies the customers using the K-means algorithm and finally scores the customers in terms of their loyalty in each cluster. In the suggested approach, first the customers’ records will be clustered and then the RFM model items will be specified through selecting the effective properties on the customers’ loyalty rate using the multipurpose genetic algorithm. Next, they will be scored in each cluster based on the effect that they have on the loyalty rate. The influence rate each property has on loyalty is calculated using the Spearman’s correlation coefficient.