COPRAS Neutrosophic Approach with Big Data Analytics for Enhancing Multi-Dimensional Customer Churn Prediction on Corporate Performance Assessment

Abstract

A neutrosophic set (NS) is a new computing technology that accesses ambiguous data through three memberships. A soft expert set (SES) is based on the concept of a “soft set” with an expert system. Now, this technique has been applied in different domains namely measurement theory, intelligent systems, game theory, probability theory, cybernetics, etc. Customer Churn prediction implies identifying which consumers are expected to cancel a subscription to a service or leave a service. It is a crucial forecast for several businesses because obtaining new users frequently costs more than holding existing ones. The Churn prediction modeling methods try to understand the accurate customer attributes and behaviors that signal the risk and timing of customers leaving. This manuscript offers the design of an AI-based Multi-Dimensional Customer Churn Prediction for Corporate Performance Assessment (AIMD-CCPCPA) technique. The AIMD-CCPCPA technique mainly aims to detect the presence of customer churns and non-churns. It involves a two-stage process. At the initial stage, the AIMD-CCPCPA technique exploits the COPRAS Neutrosophic Method for prediction purposes. Secondly, the AIMD-CCPCPA technique involves parameter selection using a butterfly optimization algorithm (BOA). The experimental analysis of the AIMD-CCPCPA model is examined using a benchmark dataset. The acquired outcomes stated the supremacy of the AIMD-CCPCPA technique equated to other models