A New Churn Prediction Model Based on Deep Insight Features Transformation for Convolution Neural Network Architecture and Stacknet

Predicting churn has become a critical issue for service providers around the world, in particular telecom operators for whom acquiring new customers is four times more costly than retaining existing ones. To keep up with the market, considerable investments are made to develop new anti-churn strategies, including machine learning models that are increasingly used in this field. In the work, the authors combine three stages. In first stage, by using deepInsight, they transform the attributes of dataset into images in order to take advantage of the strength of convolution networks in detecting hidden patterns in the dataset. In the second stage, they use deep convolutional neural network for features extraction. In the last stage, they built a three-layer Stacknet of eight selected algorithms using a successive split-grid search for classification and churn prediction. The proposed model obtained the best accuracy score of 83.4%, better than the other proposed models in the literature. © 2022 IGI Global. All rights reserved.