Research On Methods Of Customer Value Determination Based On Cloud Model

Contemporary enterprises develop rapidly with increasingly fierce competition forcustomer resources. How to improve customer relations, acquire customer evaluation, getreasonable clustering customers, to improve customer satisfaction is a modern problem forenterprise to go deep thinking, research, put in financial investment and human resources.Based on the customer clustering, the evaluation model is a respectively hot research fieldand give range of applications for the financial, construction, customer management andother aspects.In the modeling process of the evaluation model, involve a large number ofprocessing of customer feedback, most sources of knowledge are natural language, areoften uncertain, and therefore, in order to build a more rational, objective research modelfor studying，it is very necessary to deal with these uncertain knowledge theory andmethod. On this basis, for different clustering of customer satisfaction, which indicatorscould be used and clustering algorithm are discussed in this article.In this study, as the traditional evaluation method of clustering come into distortion orinvalid cases, this paper dealt with uncertainty problem, focused on solving customerevaluation models-the customer level clouds and level of customer value model buildingcustomers on the basis of comparison of the various clustering methods, and using cloudmodel of customer evaluation cluster analysis to calculate customer value and achievecustomer clustering.The main content of the thesis and the results obtained are as follows:(1) study the customer model and method. Processing vast amounts of statistical datato distinguish between the value of the customer and determine customer value system, theuse of MATLAB and SAS for analysis, to finalize work on customer clustering.(2) proposed a comprehensive evaluation of the corporate sector cloud. Start byforward cloud Reverse cloud from enterprise indicators to establish the appropriateevaluation model and subjective fuzzy statistics, the last statistics will be obtained throughthe cloud generator of different customers and different levels of ranking results.(3) The results of the cluster analysis with cloud level cross-referenced with mutualauthentication, to further deepen the understanding of enterprise customers, provides abasis for business decisions and support.Dissertation is to build a more accurate and objective customer relationshipmanagement system for customer evaluation in-depth study of the theory and method ofmodeling, to further establish the the customers global algorithm based on K-means clustering model to achieve full sharing of customer value and collaboration services, Ithas a certain research value and practical significance..