Consumer Behavior Study In Telecom Enterprises Based On Data Mining Technology

The marketing cost of mobile enterprises is increasing because of the fierce competition and customer transfer cost reducing. Meanwhile, customer expectation to service is constantly improving, which leads the results of marketing services not to reach the customers'expectations. So how to use limited resources to implement precision marketing by studying customer behavior to satisfy customer needs and Improve customer satisfaction and stickiness in the context of the fierce competition and customer service requirements constantly increasing, which plays a important strategic role for mobile enterprises.In this study, the author initiates the research method on customer behavior in mobile enterprise based on data mining technology. Firstly, the author expounds data mining technology and consumer behavior theory. Secondly, the author proposes the fundamental procedure for mining consumer behavior data:defining the problems, collecting and pre-preparing data, selecting skill or algorithm, building and estimation model, and explaining the mining results. Finally, the author proposes the research content for mining consumer behavior data: customer segmentation and analysising features of the customers by using clustering method, finding customer consumption patterns by using method of association rules, predicting customer purchase predisposition by using method of classification and artificial neural networkIn this study, the author validates the consumer behavior analysis method based on data mining technology by empirical work with a part of real data from a mobile enterprise database and demonstrates that it is scientific and credible to using data mining technology to analysis customer behavior. The author put forwards that data mining method also can be applied to study economic issue such as consumption composition and consumption trend of inhabitant in a certain period. Meanwhile, Data-driven consumer behavior analysis proposed in this study provides strong data support to implement precision marketing and personalized recommendation for mobile enterprises.