Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

Abstract

Presently, customer retention is essential for reducing customer churn in telecommunication industry. Customer churn prediction (CCP) is important to predict the possibility of customer retention in the quality of services. Since risks of customer churn also get essential, the rise of machine learning (ML) models can be employed to investigate the characteristics of customer behavior. Besides, deep learning (DL) models help in prediction of the customer behavior based characteristic data. Since the DL models necessitate hyperparameter modelling and effort, the process is difficult for research communities and business people. In this view, this study designs an optimal deep canonically correlated autoencoder based prediction (O-DCCAEP) model for competitive customer dependent application sector. In addition, the O-DCCAEP method purposes for determining the churning nature of the customers. The O-DCCAEP technique encompasses pre-processing, classification, and hyperparameter optimization. Additionally, the DCCAE model is employed to classify the churners or non-churner. Furthermore, the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm (DHOA). The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.