Prioritization of social media advertising channels inonline retailing by an ai-driven mcdm framework

Abstract

This research aims to bridge the current gap in online retail advertising by developing an AI-powered Multi-Criteria Decision Making (MCDM) framework by combining the Analytical Hierarchy Process (AHP) with Artificial Intelligence (AI). The main objective of the research is to prioritize key evaluation criteria and strategic options to help online retailers improve their social media advertising strategies. In this study, a comprehensive literature review was conducted to determine the key social media advertising channels and the criteria to be used to evaluate these advertising channels. The determined criteria were validated by marketing experts and the list was finalized. Then, the criteria were prioritized with expert opinions and the judgments of AI tools such as ChatGPT and Gemini. In this context, the pairwise comparison questions suggested by the AHP method were asked to experts and AI tools. In the next stage, experts and AI tools scored six strategic alternatives: Data- Driven Advertising, Content-Driven Strategy, Optimized Campaigns, Influencer Partnerships, Gamified Advertising Campaigns, and Social Responsibility-Based Advertising. The results were checked to see whether the data fit the experts' criteria and if the prioritization of the tools for AI was correct. Then the importance of the criterion was checked for each option rating through sensitivity analysis as a step. This research is likely to unveil the most important management results for a better resource allocation and strategic efficiency in social media marketing. This analysis will provide valuable managerial insights for optimizing resource allocation and strategic effectiveness in social media advertising. The aim of this study is to support online shopping platforms in improving their social media advertising strategies and to present a multi-criteria decision-making framework that evaluates and ranks key factors to enable decision makers to use resources more effectively. As a result, it is expected that this study will reveal valuable managerial implications for businesses.