THE IMPACT OF ARTIFICIAL INTELLIGENCE ON PRODUCT LISTING OPTIMIZATION IN MARKETPLACES

Abstract

With the continued growth of online marketplaces, competition for consumer focus has become even more fierce, especially for product listings as they become the determining factor for consumers between sellers and platforms. As marketplaces grow in both size and complexity, traditional, online, and rule-based optimization become less effective in the face of dynamic consumer behavior, multi-modal content, and systematic, ranking algorithm variations. To engage with the most recent literature in e-commerce marketing, information systems, and machine learning, this study focuses on the application of AI technologies, such as machine learning, natural language processing, and computer vision, along with recommendation systems. The study analyzes the effects of these technologies on the listing processes of generation, evaluation, modification, and of the optimization process. AI optimization in product listings has the ability to increase visibility, engagement, and conversion. The study also highlights the possible negative effects of complete automation, content homogenization, algorithmic opacity, and the lack of transparency. The results indicate optimization of product listings via AI is the first of many steps to a continuous, data-driven, and adaptable approach to marketplace automation. The balanced governance of these systems still relies heavily on the insight of the users to maintain a level of transparency and fairness. Finally, the study attempts to improve seller and platform understanding of the automation processes and the anticipated shifts in market conditions as a result of AI.