Reducing size- and fit-related returns in fashion e-commerce : Exploring AI-driven virtual fitting solutions – A study on the benefits and challenges from the retailer’s perspective

Fashion e-commerce has experienced exponential growth, offering convenience and accessibility to consumers worldwide. However, high return rates, particularly due to size and fit issues, present significant financial and environmental challenges for retailers. Virtual Fitting Rooms (VFRs) have become evident to address this issue by enhancing the online shopping experience and reducing uncertainty around fit. Despite their increasing adoption, there remains a gap in understanding how retailers perceive the benefits and challenges of VFR technologies, particularly those driven by AI. This study explores AI-driven virtual fitting solutions and their potential to reduce return rates, with a focus on their feasibility and long-term sustainability in fashion e-commerce. Using a mixed method research approach, this study employs qualitative semi-structured interviews with fashion e-commerce retailers that utilize various AI-driven VFR solutions. The aim is to evaluate how these technologies impact return rates and gain an understanding of the retailer's perspective and experience with these services. Additionally, a quantitative approach incorporating the collection of quantitative data, is used to provide insights into the measurable effectiveness of AI-driven VFRs in reducing return rates and increasing conversion rates. The study found that while fashion retailers recognize the potential of AI-driven VFRs to improve size accuracy and reduce returns, many remain skeptical about their real-world effectiveness. Challenges such as technological limitations in simulating fabric behavior and persistent consumer habits, like ordering multiple sizes due to free returns, limit the impact of VFRs. This suggests that high return rates are not just a fit issue but also influenced by broader consumer behavior and systemic factors. Additionally, retailers reported short-term use of AI-driven VFR solutions, often switching providers quickly without long-term evaluation frameworks, making it difficult to assess true effectiveness. The importance of product category differences was emphasized, with higher fit expectations in performance wear. Retailers also emphasized the need for better analytics from VFR tools to gain insights into consumer behavior and returns, which could help unlock the technology’s full potential over time.