Research On The Application Of Data Mining In The High-Value Customer Relationship Management In The Mobile Telecommunications Industry

As market competition intensifying and business developing step-by-step in the mobile telecommunications enterprises , the running mode of the enterprise is approaching to the client at the center, information support, and the data model based on the internationally advanced. In this process of change, customer relationship management become the prerequisite and basis for the realization of scientific management. In the huge corporate client group, the focus of high-value customers, as profit contribution, play a decisive role for the survival and development of enterprises , and become the core in enterprise resources. Meanwhile the emergence of data mining technology for mobile telecommunications companies provides the powerful decision support information. It can analyze historical data to achieve a more reasonable customer orientation, and develop more effective marketing decisions. Therefore, research on the application of data mining in the high-value customer relationship management in the mobile telecommunications industry is of great theoretical and practical significance.This paper reviews relevant literature of the data mining technology and customer relationship management firstly , and analyses the necessity and feasibility of the mobile telecommunications industry using the data mining for customer relationship management, then gives accurate definition of high-value customers in the mobile telecommunications industry based on the theory discussion of the high-value customers. Based on a comprehensive theoretical study, this paper determines the research objective is the high-value customers. According to the research methods and technology as data mining , this paper studies the related theme of the mobile telecommunications industry for the customer relationship management .The choice of target groups as high-value customers, largely dues to the analysis thorough of the customer value. The study finds that the traditional researches on the customers from the current value, are focused on the large customers, and ignore the impacts of other time, monetary and non-monetary dimensions. I review the relevant literature , and make the strict separation and accurate definition about high-value customers from multi-dimensions or multi-levels . That is not only laid a good groundwork for the analysis of customer relations, but also help the enterprises to resolve the targeted resources and realize the diversity market vendition.In the normative analysis, I have considered a variety of standard business data mining processes, and proposed a specific methodology combining the characteristics of telecommunications industry. The methodology contains objectives of commercial issues, data acquisition, data preprocessing , establishment and optimization of the model, and finally the assessment of the model.By combining the actual data from a certain province mobile corporation , the paper analyses detailedly the actual existent problem in the mobile telecommunications industry, and studies the customer relationship management relative theory from the diverse angle of view . Finally the paper selects the customer segmentation , cross-selling and customer loss forecast based on customer value as the study content , and actualizes the overall enterprise customer relationship management. This paper concentrates mainly three aspects in the following.(1)About the customer segmentation based on customer value, this paper makes the high value clients in the mobile communications industry for accurate positioning through a comprehensive analysis and understanding. The high-value customers are given in-depth coverage of understanding that the value should include the current value and high potential value. From the perspective of analysis the high-value should also include the value of non-monetary value and monetary value. On the basis of literature in past, the paper brings forward a PCN double standard model to actualize the value-based customer segmentation. The model will spread to a number of parameters to describe customer value, and all parameters through a standardized method will be integrated an indicator. Subsequently the paper uses the cluster analysis technology in the data mining theory and the K-means algorithm to find a true high-value customers. Through the application research on the historical data from a certain province , I found that the model can achieve an effective customer segmentation , acquire the features of all types of clients, and find the high-value customers.(2) After finding the high-value customers through customer segmentation model, because of its importance in the profit contribution, this paper establishes a cross-selling model for the cross-sell analysis of high-value customers. The paper has explained the relevant concept and found relevant rules model which uses the Mining Association Rules in data mining theory. Through Apriori algorithm finding the relationship between customers and business, it is in order to achieve cross-sell, meet customer needs and increase the enterprises profits. This paper has combined the application research on the data of the certain province's high-value customers using ring business, gained results show that the model can find the association rules which meet terms ,and realized the cross-sell.(3) Loss of customers has been one of the issues that most concern in the mobile telecommunications industry ,and high-value customers is the most important enterprises resource. Conducting research at the same time, we also have conducted in-depth and meticulous research on the predictor of the loss of high-value customers. In this research, we find that the classification algorithms in data mining can be used to predict the loss of customers. Therefore, this paper using decision tree theory, sets the historical data of loss of high-value customers as training data aggregate, and establishes the decision tree model. The model has scoring rules for the loss of clients, and offers the decision basis for the loss of future passengers .From this data mining methodology and the establishment of the three models, we can find : With customer relationship management theory and data mining theory being increasingly mature, customer management is increasingly becoming the focus of attention of mobile telecommunications operators. Because of the importance of high-value customers, enterprises take some special policy and decision. Using data mining technology to manage customer relationship in high-value customers in the mobile telecommunications industry, can really achieve efficient use of historical data, and accurately grasp the customers characteristics . Moreover, the targeted strategy can acquire maximize profits, and help to improve efficiency and expand market . Meanwhile using data mining technology can identify trends of the loss of customers and take timely measures to retain customers.