ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry

Abstract

In the Telecommunication Industry (TCI) customer churn is a significant issue because the revenue of the service provider is highly dependent on the retention of existing customers. In this competitive market, it is essential for the service providers to figure out the concerns of their existing customers regarding their services as the cancellation of the services by the customers and switching to new service providers will not bring any good to the service provider. In the context of TCI, numerous research have been made to predict customer churn though, after the performance evaluation of these studies, it shows that there is enough room for progress. Therefore, in this study, we proposed a novel customer churn prediction architecture namely ChurnNet to predict customer churn in TCI. In our proposed ChurnNet, the 1D convolution layer is integrated with residual block, squeeze and excitation block, and spatial attention module to improve the performance. Residual block aids in solving the vanishing gradient problem. Squeeze and excitation block and spatial attention module enable the ChurnNet to understand the interdependency between and within the channels respectively. To evaluate the performance, the experiment is performed on 3 publicly available datasets. As the datasets have significant class imbalance issues, three data balancing techniques such as SMOTE, SMOTEEN, and SMOTETomek are performed. Along with 10-fold cross-validation and after going through the rigorous experiment it was found that ChurnNet performed better than the state-of-the-art and obtained 95.59%, 96.94%, and 97.52% accuracy on 3 benchmark datasets respectively.

Keywords

• Squeeze and excitation
• spatial attention
• residual block
• churn prediction
• ChurnNet