Research on Data-Driven Optimization of Return Merchandise Resale Strategies
DOI:
https://doi.org/10.62051/ijgem.v10n7.08Keywords:
Data-driven, Return merchandise resale, Bidirectional fairness, Three-party interest balance, Algorithmic discrimination, Information disclosureAbstract
Against the backdrop of rapid development in the digital economy, China's e-commerce industry has experienced explosive growth, accompanied by the persistent industry pain point of high return rates. Return rates in fast-moving consumer goods sectors such as apparel and cosmetics are generally high, with some live-streaming e-commerce platforms experiencing return rates exceeding 60%, directly causing industry average annual loss costs to exceed standards and severely constraining the sustainable development of the e-commerce industry. Some e-commerce platforms, in an effort to reduce merchants' return costs, have introduced algorithmic strategies such as "high-refund population shielding," yet these have sparked large-scale algorithmic discrimination controversies due to single metrics and insufficient transparency, both damaging consumers' legitimate rights and undermining market transaction fairness. This paper takes "bidirectional fairness" as its core orientation, focusing on the three-party interest balance of "user-merchant-platform," and constructs a data-driven return merchandise resale optimization system integrating multi-dimensional indicators of "merchandise damage degree-user preference-merchant responsibility ratio." The research employs literature research, empirical analysis, questionnaire surveys, and case analysis methods, based on return records and user behavior data from a major domestic e-commerce platform's apparel category from 2022-2024, to verify the practical effects of the optimization strategies. Results demonstrate that the proposed optimization strategies can effectively reduce algorithmic discrimination complaints in the apparel industry, decrease industry loss costs, and significantly improve the secondary transaction rate of returned merchandise. This research fills the research gap in algorithmic discrimination avoidance in the return merchandise resale field, enriches data-driven reverse logistics management theory, and provides important theoretical support and practical reference for e-commerce platform return management practices, relevant legal and regulatory improvements, and healthy industry development.
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