Developing a model for measuring customer's loyalty and value with RFM technique and clustering algorithms

In today's competitive world, moving toward customer-oriented markets with increased access to customer's transaction data, identifying loyal customers and estimating their lifetime value makes crucial. Since knowledge of customer value provides targeted data for personalized markets, implementing customer relationship management strategy helps organizations to identify and segment customers and create long-term relationships with them, and as a result, they can maximize customer lifetime value. Data mining techniques are known as a powerful tool for this purpose. The purpose of this paper is customer segmentation using RFM technique and clustering algorithms based on customer's value, to specify loyal and profitable customers. We also used classification algorithms to obtain useful rules for implementing effective customer relationship management. This paper used a combination of behavioral and demographical characteristics of individuals to estimate loyalty. Finally, the proposed model has been implemented on a grocery store's data, during 1997 to 1998 in Singapore, to measure customer's loyalty during these two years.