Data Mining Application To Customer Value Management

Customer selection is plays an important role in online marketing. Therefore, the management of customer value is of great necessity for a business to select its genuine customers. To select the potential customers is simply a very preliminary approach and prerequisite for corporate development. Since the utmost purpose of "customer first" service is the maximization of corporate profits, a business should further probe into the real value of its real customers. To achieve this, any corporation shall, by means of data analytical instrument, strive to be informed of customer regularity and apply it into decision makings. For lack of such an instrument in the past, businesses, as a rule, would act in line with their habitual experiences when confronted with huge amounts of data, and the research on customer value mining was only scarcely conducted in customer relation management (CRM). The development and maturity of data warehouse and data mining techniques have now contributed technologically to customer value exploration.There exist, however, considerable differences at both home and abroad as to how to define and calculate customer value, and how to categorize customers. Relevant researches, on the other hand, have not come up to any application realms of customer value management (CVM), a system that is based on data mining. The applications of CVM, data warehouse and data mining in China are in their initial steps, with researchers being scattered in big research institutes and institutions of higher learning. The publications so far are mainly about data mining related regulation in terms of its algorithmic research, modification and realization. It is a sheer blank field in the application of data warehouse and data mining onto customer value exploitation, which poses a great significance and urgency to be practiced. This paper is dedicated to the research of the combination of CVM and data mining technique, and, based on the latter, of the application of customer value management.This paper consists of five chapters.Chapter one of this paper, the introduction part, deals with the research background and research purpose of this endeavor, followed by related literature review, the paper novelty and research methods.Chapter two serves as a theoretic foundation for this paper and deals with related theories. CVM theories are explained in three aspects. As for the extensions of CVM, the definitions of customer, customer value and CVM are furnished. And as the core of CVM, customer value is given more details while a further analysis of customer value creation is made. In the elaboration of CVM system, three innovative concepts are put forth: width management, perspective management and depth management.Chapter three furnishes technological support for this research. The data mining theories consist of such sections as the concept, classification, technique, procedure and application of data mining.Chapter four is the body part introducing the application aspect. The data mining-based CVM application system is devised after literature review and research. Three application areas of data mining are mainly discussed: the application of customer identification based on CVM width, that of customer maintenance/drain, and of customer satisfaction/loyalty based on CVM perspective, and that of customer contribution based on CVM depth.The last chapter introduces some related modules as the technological part, an effort to devise a CVM system based on data mining. Six business sub-modules are therefore abstracted from CRM. Centering on customer value, a business logic layer is thus composed by means of such sub-modules as customer acquisition, customer maintenance/drain prediction, customer satisfaction, customer loyalty, customer contribution and intercrossed selling.