Customer Churn Prediction for Broadband Internet Services

Although churn prediction has been all area of research ill the voice branch of telecommunication services, more focused studies oil the huge growth area, of Broadband Internet services are limited. Therefore, this paper presents a new set of features for broadband Internet customer churn prediction, based oil Henley segments, the broadband usage, dial types, the spend of dial-up, line-information, bill and payment information, account information. Then the four prediction techniques, (Logistic Regressions, Decision Trees, Multilayer Perceptron Neural Networks and Support Vector Machines) are applied in customer churn, based on the new features. Finally, the evaluation of new features and a comparative analysis of the predictors are made for broadband customer churn prediction. The experimental results show that the new features, with these four modelling techniques are efficient for customer churn prediction in the broadband service field.