Returns Management in E-commerce

Returns management in e-commerce encompasses the policies, processes and logistical systems that govern the movement of goods from customer back to retailer or manufacturer. As online shopping volumes have surged globally, return rates can account for up to half of certain product categories, placing significant strain on supply chains, warehousing and financial performance. Effective returns management requires close coordination between customer service, reverse logistics, information technology and sustainability teams. Key challenges include controlling operational costs, mitigating fraudulent claims, preserving customer satisfaction and minimising environmental impact. Recent advances in data analytics and machine learning have enabled more accurate forecasting of return volumes and identification of high-risk transactions. At the same time, strategic alignment of return policies with broader business objectives—such as circularity and lean operations—has become essential to balancing service levels with cost and carbon footprints. Practical interventions range from pre-sales tools (for example digital fitting or enhanced product visualisation) to differentiated policy tiers that incentivise responsible returns. Taken together, these developments reflect a maturing research field that addresses both the economic and ecological dimensions of product returns in a globalised, omnichannel marketplace.

Research from Nature Portfolio

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Research from all publishers

Recent studies have investigated strategic, analytical and behavioural dimensions of returns management. A 2023 alignment-perspective study proposed a conceptual framework identifying seven critical misalignments that undermine policy effectiveness, offering managers a roadmap for harmonising return processes with supply chain orientation. Another 2023 enquiry into fraudulent returns in multichannel retail highlighted the rise of new fraud types, analysing enabling factors and proposing a layered mitigation framework that combines procedural controls with enhanced authentication and customer profiling. In 2024, a systematic review of forecasting methods surveyed the application of machine learning models to predict return volumes, classifying approaches by required data inputs, algorithmic techniques and forecast accuracy. This review synthesised best practices from information systems, operations management and marketing, and outlined future research avenues for integrating real-time customer signals and environmental considerations into predictive models.

Returns Management in E-commerce publication trend

The graph below shows the total number of articles in returns management in e-commerce across all publications each year (not limited to Nature Index journals).

Technical terms

Reverse logistics: The set of processes for handling returned goods from customers, including collection, inspection, refurbishment and disposition.

Returns policy: A retailer’s official rules governing the eligibility, time frame and financial terms under which customers may return purchased products.

Forecasting models: Analytical or machine learning tools designed to predict future return volumes based on historical sales, customer behaviour and product attributes.

Supply chain alignment: The strategic coordination of returns management activities with broader supply chain objectives, such as cost reduction, service quality and sustainability targets.

References

1. Retail returns management strategy: An alignment perspective. Journal of Innovation & Knowledge (2023).
2. Understanding fraudulent returns and mitigation strategies in multichannel retailing. Journal of Retailing and Consumer Services (2023).
3. Forecasting e-commerce consumer returns: a systematic literature review. Management Review Quarterly (2024).