A Study of Discovering Customer Value for CRM: Integrating Customer Lifetime Value Analysis and Data Mining Techniques

Abstract

Customer relationship management (CRM) has become an important strategy for businesses. Under the current competitive commercial environment, the discovering, maintenance, and strengthening of customer value is a key for businesses to earn sustainable profits. Past studies have found that customer lifetime value (CLV) can be used to calculate each customer's contribution to the company, and data mining can be employed as an analytical tool to discover customers' potential behavioral patterns and characteristics. Though both are complementary, rarely are there studies applying the two methods at the same time. This research develops a conceptual framework which combines CLV analysis and data mining techniques to enhance CRM. Firstly, we use CLV analysis to calculate customers' current value (CCV) and customers' potential value (CPV). Next, the clustering analysis is used to group customers based on each customer's CCV and CPV. Finally, a data mining method is employed to discover the characteristics and the potential purchasing behavioral patterns of each group. By establishing a customer value pyramid based on our finding and providing the marketing implications for each group, this research served as a reference for managers engaging in CRM strategy.