Building customer churn prediction models in Indonesian telecommunication company using decision tree algorithm

Abstract

Customer churn has become a big problem for telecommunication companies. Preventive efforts are needed by predicting the value of churn in the future. This study uses data mining techniques with decision tree algorithms to predict customer churn in one of Indonesian Telecommunication companies. The best decision tree model has parameters of criterion information gain with a minimal gain = 0.01 and a max depth = 6. This decision tree model has an accuracy value of 78.28% with 19,6% customer churn rate. Based on this model, customers of this company tend to have voluntary churns. Some important factors that affect customer churn are type of contract, number of monthly downloads, tenure, customer satisfaction value, and add on. The type of contract has the highest impact on the customer churn in this company. Based on the results, the company is suggested to promote a retention program based in order to decrease customer churn rate.