Intelligent Model for Customer Churn Prediction using Deep Learning Optimization Algorithms

Abstract

Business intelligence (BI) mentions to the technical and procedural structure which gathers, supplies, and examines the data formed by company action. BI is a wide term that includes descriptive analytics, procedure analysis, data mining, and performance benchmarking. Customer churn is a general problem across businesses from several sectors. Companies are working always for improving their supposed quality by way of providing timely and quality service to its customer. Customer churn is developed most initial challenges which several firms were facing currently. Many churn prediction techniques and methods were presented before in literature for predicting customer churn from the domains like telecom, finance, banking, and so on. Researchers are also working on customer churn prediction (CCP) from e-commerce utilizing data mining and machine learning (ML) approaches. This manuscript focuses on the development of Stacked Deep Learning with Wind Driven Optimization based Business Intelligence for Customer Churn Prediction model. The proposed model is considered an intelligent system that applies golden sine algorithm (GSA) based feature selection approach to derive a set of features. In addition, the stacked gated recurrent unit (SGRU) model is applied for the prediction of customer churns.