Application Of K-means Clustering Algorithm In Bank CRM

With the rapid growth of China's economy in recent years,the competition among various industries has become increasingly fierce,especially for banks and other financial sectors.How to survive in such a fierce competitive environment has become the most concerned issue.In recent years,the development of information technology has brought about tremendous changes in the competitive environment of banks.Banks are gradually realizing that grasping customers means grasping their performance,and the more they can meet the needs of customers in a timely manner,the more they can meet the needs of the market.The more able to stand out in competition with the other banks.At present,most banks in China have established an internal CRM system,and China's financial industry has also accumulated a massive amount of customer data resources.If these customer data information can be effectively used to accurately classify customers,different customer groups The provision of more targeted services will enable banks to provide more effective services and will greatly benefit the improvement of banking financial services.However,how to effectively use existing information and dig out information that is truly useful to bank decision makers is an important research topic.The research of clustering algorithm has become a very active research topic in data mining technology research.The K-means algorithm is the most widely used clustering algorithm based on iterative reassignment of squared errors.This paper focuses on the application and implementation of K-means algorithm in the direction of bank customer classification.First introduced the basic theory of data mining,then introduced the basic theory of clustering analysis algorithm,introduced the K-means clustering algorithm in detail,analyzed the advantages and disadvantages of the algorithm,and then defined the bank customer classification theory system for the later Bank customer clustering provides theoretical support.In terms of regional,industry product preferences,and trading activity,we profoundly recognize the significant features and group differences that are present and valuable to the corporate customers.We combine these empirical knowledge in the business data and explore through clustering.It is the focus and breakthrough of fine-tuned customers,achieving the goal of intensive and accurate marketing in customer management.This paper takes the customer transaction data of a bank in Jiangsu as an example.Through a total of 38 kinds of financial linkages between different financial products of the bank,the correlation analysis is used to find out the product association rules behind the data,and the K-means clustering algorithm is used.,classify all 38 kinds of combinations,and through the control of the degree of support and confidence,we screen out a set of effective associations with high generality and high possibility of sales.Subsequently,K-means clustering algorithm was used to classify customer transaction activity and customer product preferences.Finally,after identifying the opportunities for cross-selling with corporate customers,the three classification results were aggregated through the combination of product grouping,customer activity grouping,and customer product preference grouping.We generated a detailed cross-selling target customer list.In order to provide first-line business personnel with more accurate and full-scale guidelines,the contents not only provide cross-selling recommendation products or product packages for each target customer,but also include basic customer information,customer activity grouping,customer product preference grouping,etc.This allows the client manager to understand the customer's understanding at a glance,so that they can be confident and confident that "only knowing oneself can achieve the best." It also enables the bank to have a very intuitive and clear understanding of the customer characteristics of its own customers,product preferences and products that may be of interest in the next phase,which is conducive to the development of bank-specific products and can enhance the customer's bondability.Dig customers.It is possible to manage different customer groups separately,allocate bank financial resources more effectively,avoid risks that can be avoided,and maximize profits.