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

Jalal Rabbah · Mohammed Ridouani · Larbi Hassouni

16 citations0/0 files storedInternational Journal of Web-Based Learning and Teaching Technologies

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Customer churn and segmentation Data Mining and Machine Learning Applications AI and HR Technologies

Abstract

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 proposed work, we combine three stages. In first stage, by using deepInsight, we transform the attributes of dataset into images in order to take the advantage of the strength of convolution networks in detecting hidden patterns in the dataset. In second stage, we use deep convolutional neural network for features extraction. In the last stage, we 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 others proposed models in literatures.

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2018Customer churn prediction in telecommunication industry using data certainty 2012Applications of Data Mining Techniques in Telecom Churn Prediction 2017Comparison of Deep Learning Algorithms to Predict Customer Churn within a Local Retail Industry 2012Customer churn prediction for telecommunication: Employing various various features selection techniques and tree based ensemble classifiers 2016Churn analysis using deep convolutional neural networks and autoencoders 2019Churn Prediction with Sequential Data and Deep Neural Networks. A Comparative Analysis 2014A Comparative Assessment of the Performance of Ensemble Learning in Customer Churn Prediction

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