The Study And Application Of A Prediction Model Of Telecom Customer Churn Based On Data Mining Technology

Data mining technology makes use of existed data to find out the underling business rule by establishing mathematical model. The prediction of customer churn in telecommunication industry is very important. The prediction of customer churn is to analyze the churned customer's historical data .So that the reasons why they left might be found out. It will help the telecom company to adopt measures early to reduce customer churn. It has a very important significance for enterprises to reduce operating costs and enhance operating performance.In order to improve the efficiency of data mining, the thesis proposes a data mining method based on rough set and artificial neural network. By reduction processing to the import space, this method adopts artificial neural network for data mining on the reduced training data. The method exerts the ability of rough set's reduction knowledge and the high precision feature of artificial neural network. It gains a very good result to apply this method to the prediction of telecom customer churn.The thesis builds a telecom customer churn prediction model, guided by the above data mining techniques. And a prototype is implemented and evaluated on real data. The results of evaluation show that the prediction model is feasible. The prediction model helps to predict customer-churn behavior in the telecommunication industries.