Customer Subdivision Research On CRM Based On Data Mining In Securities Industry

With constant aggravation of the securities market competition, how toimplement difference marketing and service to different customers group, how tosubdivide and classify the active demand that has already become presenttelesecurities enterprises to the customer. This article has discussed the current situations of enterprise's customer ofsecurities subdivides and development trends of the work at present, and has analyzedthat excavates technology of the data to apply to the necessity and feasibility insecurities enterprises, is it excavate data cluster analysis apply to customer , securitiesof enterprise subdivide and work to propose. The association classification model based on the support-significantstrcture,improved upon the traditional association classification model,is proposed toaddress the problem of complexity and implicitness shown in customers'behaviors,and to overcome the usual deficiencies in existing data mining methodscaused by lack of support of rigorous statistical methods.The customer segmentationmethods derived from this new model can guarantee the statistical significance of thediscovered knowledge and increase the ability to discover implicit rules.Lastly ,based on the work outlined above,a real example is used to analyze theapplication of customer oriented data mining to CRM.This thesis solves some of thecritical problems and deficiencies of the existing data mining methods when applyingto customer segmentation.Its results will contribute greatly to future applications ofdata mining to CRM.