Data Warehouse And Data Mining In Customer Relationship Management Of Commercial Banks

Withthe development of computer technology, data warehouse and data miningtechnology is increasingly applied to various industries, especially the industries which storethe massive amount of customer information：such as telecoms, finance, retail, etc.Competition among these industries is ultimately belong to the customer’s competition,therefore, it is crucialto grasp the customer information and excavateavailable informationfrom numerous customer’s information.It is useful for enterprises to analyse customers’information, to predict customers’ consumption trends, to retain existing customers and findpotential customers using data warehouse and data mining technology, so as to reduce thecosts of the enterprises, increase the income of the enterprises, make the enterprises in animpregnable position in the competition.This paper mainly studies the application of data warehouse and data mining technologyin bank customer relationship management(CRM).Taking the customer information as studybackground and using the data warehouse and data mining technology, the paper mainly dothe following work：(1)The data warehouse model was established based on the DB2database.The modelcontain ODS, EDW and DDWmode.The data import between modes is done by ETL.(2)Using OLAP which belongs to SQLServer BIDS assembly to analyzethe informationof customer accounts, customer’s branch of the bank loan accounts and transaction.(3)The data in the warehouse was processed by using data mining technology. Alalyzesthe advantages and disadvantages of DBSCAN, K-Means and X-Means algorithm using datacase.Finally X-Means algorithm was adopted for Individual customers segmentation aftercomparing.On the basis, the cross-selling of bank business was realized by using FP-Growthalgorithm.The content of the paper is of certain practical and theoretical significance, and it canmake scientific decision support for the bank’s policy makers.