Optimizing Deep Learning ANN Model to Predict Customer Churn

Customer churn indicates percentage of customers that are no longer using a company service in a certain time. Thus, it leads to decrease of the company profit. PT. XYZ, one of Telecommunication Company in Indonesia, approximately has a churn rate of 18%, therefore the company needs to focus its improvement in order to lower the churn rate. This study aims to build a classification model based on optimized deep learning ANN algorithm in order to predict customer churn rates. Deep-learning ANN has an advantage of its flexible characteristic. The result show that the optimized ANN model has an accuracy value of 76,35%. The model is built with an epoch parameter of 30, hidden layer =50 with tanh as the activation function. The contract type, type of service, and IPTV are the three most influential variables in customer churn at PT. XYZ. The prediction results in the optimized deep learning-ANN model indicate that there is 2567 customers tend to be churn customers and 4386 customers tend to be loyal customers. The churn percentage in this model is 36.4%. There is an increase of 11% compared to the previous year.