Research on customer churn prediction model based on IG_NN double attribute selection

This paper discusses the problem of customer churn prediction, and proposes the customer churn prediction model based on double attribute selection of information gain (IG) and neural network (NN) by analyzing the characteristics of customer churn data. That is, firstly, undertake the main attribute selection for customer churn data by using IG, and then analyze every main attribute by using NN, which output results are analyzed by 80–20 rule to get the key attributes affecting customer churn; secondly, construct the prediction model based on IG_NN by taking the key attributes as input and customer churn probability as output. The model predicts lost customers next month by carrying on data acquisition about customer behavior and payment information of a telecom operator during first three months. Provably, there is improvement of various degrees of accuracy, coverage rate and hit rate than other methods for customer churn prediction. This model has a good prediction performance for dealing with a large quantity of non-equilibrium data set.