Data Mining Technology In Customer Relationship Management System In The Supermarket

In recent years, Customer Relationship Management(CRM) is accepted by various enterprises. Through the establishment of customer relationship management system, these enterprises can formulate appropriate, targeted marketing policies to improve customer relations, increase sales, enhance their competitiveness.However, as customer relationship management system goes in-depth, we obviously feel that the current customer relationship management system facing a huge enterprise data storage, it is somewhat insufficient. Therefore, it became inevitable that data mining technology which can find knowledge from enormous data was applied to the customer relationship management system.This thesis tries to implement some data mining methods on the customer relationship management system of Jiangning SuGuo supermarket , to help the supermarket business making marketing decision. This thesis uses association rules,classification analysis, clustering analysis, sequence patterns and personalized recommendation to conduct five such applications, and analysis&implementation of the main Aproiri algorithm, FP_tree algorithm, C4.5 algorithm, naive Bayes classification algorithm, k-means algorithm , PAM algorithm, DBSCAN algorithm, AprioriSome algorithm and Web-based collaboration of Mining and screen personalized recommendation to the realization of the Jiangning SuGuo supermarket to improve cross-selling of goods, shelf design, customer classification, groups of customers and improve the e-commerce site personalized service and made a useful attempt, and received some very encouraging results.