Mobile Customers False Based On Data Mining Research, Off-grid

Along with the rapid development of communication industry, the competition among the carriers is getting more and more drastic. Customer resource has become the key of enterprises' competition. Nowadays almost each carrier faces a serious problem—customer churn. Just for a great quantity of customers' vanishment, it has brought great loss to many companies. And as the communication market becomes saturated, acquiring the new customer is getting much more expensive than retaining the existing customer. In such a severe circumstance, it has become one of the focuses of operation corporations that how to avoid customer churn and carry out retainxnent.The reason of customer churn varies, but not all the customers are really lost and there is little research on it. So this paper proposes the concept of illusive offline, that is some online customer joins the net again in same(or different) name after he leaves the net temporarily for all kinds of reasons.This paper introduces data mining firstly, the technology of studying illusive offline, which can reveal instructive but buried information and knowledge hidden in a large, incomplete, fuzzy, stochastic data. The paper introduces the Behavior of Offline Customer Analyse System next, which is designed to analyse offline customers and predict customer churn, describes the system's whole framework, logic framework, network structure and data organization.According the basic processes of data mining, this paper studies the subject of illusive offline at last, proposes the degree of number superposition, the degree of calling habit and other criterions base on customer's calling behavior to judge whether a customer is illusive offline. Related models and arithmetic ways are established, which use mathematics statistics, decision tree as well as other techniques. The model is adjusted and the system's performance was optimized via analyzing the data results.This research does the academic preparation work for acquiring the true offline customers, and provides help to study the problems of offline causation, saving customers and reducing the rate of customer's churn. It also forms the base for realizing the whole operation of the decision system.