Research And Application Of Data Mining In Banking Customer Relationship Management

Commercial banks are more and more difficult to compete in the product to gain a competitive advantage for higher transparency of service and product, smaller differences and shorter life cycle. The competition between the banks is changed from the competition of the products to the competition of customers, so owning a client means having a market and wining in the fierce competition. Nowadays, customer demand is increasingly personalized and diversified, and banks rely on a single product or service is difficult to meet customer needs. The traditional market strategy, which is the center of financial products, is gradually replaced by customer centered market strategy. The new challenges are how to use a large amount of customer information, deposit and loan account information and various transaction data for strengthen customer relationship, master business rules and mining customer value, and how to How to use customer information for actual bank decision making. Data mining technology make all these possible.This paper takes data mining technology in the bank customer relationship management application for the research main content. I make market segmentation from customer value, and I make bank product optimization and providing support for marketing service strategy as a modeling target. According to the balance of customer and operating profit, I classify customers by using clustering method and make it as a basis for the search for the law of customer business dealings. And I calculate the probability that a customer transfers from one customer to another by using Markov chain method. In this research, closed-loop control includes five steps, which are exploring the law through customer service record, customer classification based on clustering analysis, customer value measurement, customer segmentation based on customer value and a strategy based on data mining CRM.The paper carried out the research and testing by taking the customer data of a branch of Wangcheng Rural Commercial Bank as an example, and it completed a complete data mining and CRM fusion application of the closed-loop process from the data acquisition to customer segmentation and meticulous marketing analysis. The research results will provide data support for the management of Wangcheng Rural Commercial Bank. If the results of the data mining model could be integrated into the banking business marketing activity and to promote the corresponding new products and services for different customers, the validity of this method will be demonstrated by practice.