A Proposed q-Rung Orthopair Fuzzy-Based Decision Support System for Comparing Marketing Automation Modules for Higher Education Admission

Abstract

With rapid developments in Information and Communication Technology (ICT) sector prevailing over the present age of Industry 4.0, the nature of traditional marketing has undergone a paradigm shift. The recent pandemic has reflected the need of more emphasis on technology-enabled marketing. It is said the battle is now on digital space. Digital Marketing (DM) has become a key strategic advantage for the organizations. The age of Industry 4.0 has enabled generation of a massive volume of data at lightning speed with extremely high variety and capturing with improved hardware technology. However, it is important to derive actionable insight out of the data for formulating appropriate decision on time. Here comes the importance of marketing automation tools that help in proper organization of captured data from various sources including social media on a real-time basis for aiding the strategic decision-making process and action planning. Likewise, all sectors, education industry, particularly higher educational institutes (HEI) have witnessed a transformational change in recent years in the admission process. With more reliance on digital media, the HEIs have felt the need to acquire, nurture, engage and optimize the potential inquiries or “leads” for enhancing the enrollment. In this context, the present paper aims to compare a set of recently developed marketing automation tools such as chat BOT, Whatsapp Business API, Google Tracking, etc. from the perspective of effective lead management and optimization for improving the return on investment (ROI). For this purpose, in the present paper we propose a novel extension of a very recently developed multi-criteria decision-making (MCDM) algorithm such as Preference Ranking on the Basis of Ideal-Average Distance (PROBID) with q-Rung Orthopair Fuzzy (qROF) information wherein the criteria weights are determined by using qROF-based entropy method. We carry out the sensitivity analysis and validation test that reflect the stability and validity of the result.