With the primary objective of reducing pre-order returns, the emergence of artificial intelligence holds promise in mitigating the high rate of net returns.
A study published in the Journal of Retailing revealed that e-commerce orders experience a return rate of 30%, significantly higher than the 9% return rate observed in brick-and-mortar stores.
A significant focus of technology development lies in improving recommendations, bolstered by various online try-on functionalities for products such as clothing, footwear, and cosmetics. These features enable customers to visualize how a product will look on them before finalizing the purchase, thereby minimizing post-purchase regrets and returns.
Research by McKinsey indicates that issues related to size, style, and fit contribute to a staggering 70% of all online returns within the fashion industry.
MySizeID, an omnichannel e-commerce platform, leverages AI to assist website visitors in determining the most suitable size based on their body type.
Ronen Luzon, the CEO of MySizeID, recently shared with Fox Business that their platform can provide size recommendations to customers. For example, if a customer selects a larger size but a medium is more appropriate, the system will prompt the customer accordingly. By understanding customers’ sizing preferences, MySizeID aims to reduce return rates associated with sizing discrepancies.
Similarly, ShoeAI and Volumental utilize AI to address issues related to clothing fit. These companies utilize data on customers’ existing footwear and successful sizing experiences to offer personalized recommendations. Through the Volumental mobile app, consumers can use their smartphones to assess their foot dimensions accurately.
According to Brent Hollowell, CEO and GM of Volumental, the utilization of machine learning enables the system to provide tailored recommendations based on individual foot characteristics, thereby enhancing the likelihood of finding the most suitable footwear.
Another key application of AI in reducing returns is enhancing the accuracy of product information and addressing consumer inquiries effectively. Stitch Fix employs an AI-driven engine to analyze user data, including body measurements and style preferences, to generate customized product descriptions for each customer. Recently, Amazon and Shopify have also introduced AI tools to assist sellers in optimizing product descriptions, driving conversions, and minimizing returns.
Robert Tekiela, VP of Amazon Selection and Catalog Systems, highlighted the potential of AI models to refine and enrich product information significantly. By leveraging diverse data sources and advanced algorithms, these models continuously improve their ability to curate product data effectively.
Moreover, AI is employed in targeted search advertising to identify customers with higher profit potential. James Poll, CTO of Acorn-i, emphasized the role of AI in enhancing advertising targeting to reach customers likely to generate higher returns, thereby optimizing marketing strategies in e-commerce platforms.
