Strategic design of return services using return data in digital retailing

Purpose Historical return data represent a valuable yet underutilized source of customer insights. This study aims to develop a theoretical framework examining how online retailers can leverage such data – specifically, the level and dispersion of customer return costs – to inform service design choices between return freight insurance (RFI) and return pickup service (RPS). Design/methodology/approach This study develops game-theoretic models under both monopolistic and competitive market structures, comparing two prevalent return service options: RFI and RPS. Return cost heterogeneity is captured using the mean and dispersion of historical return data. Findings The study finds that in monopolistic markets, RFI is more effective when return costs are uniform, while RPS performs better under high cost dispersion. In competitive markets, this pattern reverses due to market segmentation. Moreover, retailers adopt divergent pricing strategies based on their return policies in response to competitor entry. Practical implications Retailers can leverage historical return data to segment customers, optimize return service design and enhance reverse logistics efficiency. Our study reveals the value of return data in digital supply chain management. Originality/value This study is among the first to model the strategic role of return data in service design for digital retailing. It contributes to the literature by linking return cost distribution with optimal service strategies, providing theoretical and practical guidance for data-driven return policy design.