Retail Platform Analytics: Practice, Literature, and Future Research

Abstract

The explosive growth of retail platforms over the past decade has resulted in a significant amount of customer and seller data that can be leveraged for advanced business analytics. As a result, the management of retail platforms with business analytics capabilities has garnered increased attention in the field of operations management. Despite the recognition of the importance of business analytics techniques for retail platforms, a systematic study of their operations is lacking in the literature. Based on our observations of the industrial practice and understanding of the academic literature, we attempt to address this gap by proposing a framework that broadly categorizes retail platform management into three key themes: demand-side management, supply-side management, and matching. For each theme, we identify critical topics, discuss the current practices of platforms, and review relevant literature. We also propose future research questions with directions for the initial modeling and solution strategy, together with applicable data sources and potential insights. At last, to facilitate future research, we provide a roadmap and datasets for further exploration of business analytics applications of retail platforms. Overall, this paper lays a strong foundation for researchers to delve deeper into the exciting and constantly evolving field of retail platform analytics.