Abstract
The telecommunication sector becomes popular in recent years and service companies suffer from a loss of valued customers to competitors. In the operational area, customer churn is a challenging issue, which makes companies, loses customers, and therefore is of bigger issue for the industry. Recent technologies and competitors were developing quickly and churn prediction becomes essential for telecom companies. Customer Churn Prediction (CCP) models offer precise detection of significant churners and therefore a retaining solution can be offered to them. Data analytics and machine learning (ML) models can be designed and used for churn investigation to reduce customer churns and improve profit. In this aspect, this article investigates a novel Tomtit Flock Optimization with Deep Variational Autoencoder for Customer Churn Prediction (TFODVAE-CCP) in the telecommunication sector. The presented TFODVAE-CCP technique mainly concentrates on the identification and classification of customer churns. To accomplish this, the presented TFODVAE-CCP technique performs data pre-processing at the primary stage. Next, the TFODVAE-CCP technique employs TFO algorithm to elect an optimal subset of features and thereby improve classification accuracy. Finally, the DVAE method can be leveraged for classifying churner/non-churner. A detailed set of experimental analyses is made on telecommunication churn dataset and the results illustrate the betterment of the TFODVAE-CCP method over other existing models.
Keywords: Customer Churn Prediction (CCP), Intelligent Models, Machine Learning (ML), Metaheuristics, Feature Selection, Telecommunication.