What's Happening?
Pascal Richter, Director Growth Continental Europe at SCAYLE, has outlined five ways artificial intelligence (AI) can help online fashion retailers reduce high return rates. A survey by SCAYLE, 'Inside the Boardroom: Fashion Leaders on the Agentic AI Era,'
found that 54% of senior executives believe size and fit make fashion more complex than other retail sectors, with 38% citing high return rates as a major issue. DHL's 2025 survey also revealed that over half of online shoppers returned items due to incorrect sizing. Richter suggests that AI can leverage existing data, such as orders, return reasons, and reviews, to prevent returns before checkout. Proposed solutions include AI-based size recommendations, showing products on diverse body types using generative AI, identifying and addressing 'bracketing' (ordering multiple sizes) in the cart, adjusting checkout terms based on customer return behavior, and using computer vision to inspect returned items for recurring defects.
Why It's Important?
High return rates in online fashion retail represent a significant financial burden and environmental concern for U.S. businesses. In the UK alone, returns of non-food goods are projected to reach £25.1 billion (approximately $33.4 billion USD) in merchandise value in 2025. For U.S. retailers, reducing returns can lead to substantial cost savings by minimizing reverse logistics expenses, preventing product devaluation, and decreasing transport emissions. Implementing AI solutions can improve customer satisfaction by ensuring better fit and reducing the inconvenience of returns, thereby fostering customer loyalty. This shift towards AI-driven personalization and operational efficiency can enhance the profitability and sustainability of the U.S. e-commerce fashion sector, making it more competitive and environmentally responsible. Retailers who adopt these technologies stand to gain a significant advantage in the market.
What's Next?
Fashion retailers are expected to increasingly integrate AI into their e-commerce platforms to address the challenge of returns. The adoption of AI-powered size recommendation engines and generative AI for diverse product imagery will likely become more widespread as companies seek to enhance the online shopping experience and reduce uncertainty for consumers. Retailers may also begin to implement more dynamic checkout terms, using machine learning to assess return risk and tailor payment and delivery options accordingly. Furthermore, the use of computer vision for inspecting returns could lead to more efficient identification of product defects and sizing inconsistencies, allowing for quicker corrective actions. These advancements will drive a more data-driven and personalized approach to online retail, potentially setting new industry standards for customer service and operational efficiency.
Beyond the Headlines
The application of AI in reducing fashion returns has deeper implications for consumer trust and the ethical use of technology. By providing more accurate sizing and realistic product representations, AI can build greater confidence among online shoppers, mitigating the need for 'bracketing' and reducing the environmental impact of excessive shipping. However, the use of AI to adjust checkout terms based on individual return behavior raises questions about fairness and potential discrimination, requiring careful consideration of ethical guidelines and transparency. The ability of AI to identify product defects also empowers retailers to improve quality control and supply chain management, leading to better products and a more sustainable production cycle. Ultimately, this technological integration could redefine the relationship between consumers and fashion brands, fostering a more informed, efficient, and responsible retail environment.
