Study On Customer Segmentation On Credit Card Based On Customer Lifetime Value And Customer Behavior

Nowadays, customers have been the competitive focus in banking. But banks (?) our country lack customer analysis. The service that the banks offer lack pertinence, the identification of high-quality customers is weak, and marketing strategies are monotonous. At the same time, it increases credit risk to some extent. With the continual growth of customers' service level in Chinese banks, we should evaluate customer value and subdivide the market in order to distinguish customers and adjust marketing strategies of credit cards. Banks can enhance marketing competition itself through different marketing strategies.Based on the theories of customer segmentation, customer segmentation methods and customer lifetime value, the paper studies customer segmentation model on credit card in Chinese banking from two perspectives, customer value and customer behaviors.Firstly, the paper introduces three theories: customer segmentation and research situation of customer segmentation on credit card in Chinese banking; customer segmentation methods from two perspectives, customer value and customer behaviors. customer lifetime value includes the definition, computing models, and affecting factors of CLV.Secondly, the paper improves customer lifetime value model to put forward a new model which considers marketing word-of-mouth effect. Then the paper analyzes customer lifetime value model of credit card industry based on the characteristics o(?) customers in credit card industry. The paper also gives marketing strategies based on customer lifetime value. It is hoped that the modified model can reflect the state of customers' value more objectively and realize classification of customers with different values.Finally, the paper uses LRFM model to analyze customers' behavior. The LRFM model was modified from traditional RFM model by introducing L as the relationship length between customers and company as well as loyalty of customers' behavior. Then the paper uses two-stage analysis method which combines k-means cluster method and decision tree method to identify customers by using credit card data. Finally, it classifies customers into four clusters: high quality customers, loyalty customers, low quality customers, and high risk customers. It uses decision tree method to analyze the cluster results and the attribute differences among them. The paper also brings out some prepositional banks' financial marketing suggestions and strategies.By using the above models to classify customers, leaders of banks can identify valuable customers and know customer' behavior more effectively. Leaders of banks can take different marketing strategies according to different valuable customers, and put limited resource to the most valuable customers, so as to enhance the competitiveness of the bank, and bring about long-term interests.