Research On Customer Segementation Model And Precision Marketing Strategies Based On Big Data

With the rapid development of mobile Internet, mobile data services become the business entity of telecom operators instead of voice services. Under the trend that Internet companies continue to penetrate the mobile communications business, telecom operators have gradually been ―piped‖. Therefore, how to break the siege, improve the quality of products and make customers‘ experience better, in order to meet the diversities of customer needs, attract new customers and retain old ones as well, is the current focus of three telecom operators in Mianland China.Big data is developing fast today; how to analyzing users‘ data by data mining to identify customer consumption habits and control customer consumption behavior, and re-design personalized and diversified services, are the key tasks for operators to form core competitive forces.In this thesis, we firstly analyse the market environment, the development status, and the efficiency of current customer segmentation method through reading a large number of literature. Secondly using the SPSS Statistic to do the correlation and collinear analysis, we obtained 15 variables, and then put them in SPSS Modeler. In this paper, Kohonen cluster analysis was used to optimize K-means cluster analysis, in some extent, to improve the reliability and accuracy of the results. Based on the basic concept of Ansoff Matrix, we build the segmentation model of telecom customers, and then compared the test set and validation set to test the results, and then put forward precision marketing strategies and suggestions for existing product development.This paper tries to combine customer segmentation with precision marketing strategy to maximize operators‘ returns ultimately, which has certain significance both in theoretical research and practical application.