In the ongoing struggle against the financial burden of online returns, apparel and fast-fashion brands like Perry Ellis and H&M are embracing a cutting-edge ally: artificial intelligence (AI). This advanced technology is proving instrumental in refining product descriptions, tailoring recommendations, and optimizing advertisements to target audiences less likely to return their purchases.
Returns pose a significant challenge for online retailers. While the return-processing costs, as a percentage of overall sales, held steady at 16.5% in 2022, the issue has become more pressing due to the impact of inflation on both consumer budgets and retailer profitability. According to the National Retail Federation, returns have remained a persistent concern, prompting a variety of responses from retailers.
A June and July survey revealed that 17% of American consumers returned at least six items in the past six months, a notable increase from the previous year’s 7.1%. This surge in returns, particularly from frequent shoppers, presents a dilemma for retailers seeking a balance between facilitating seamless transactions and avoiding the financial strain of returns.
Enter artificial intelligence. Brands are leveraging AI to devise more accurate product descriptions and recommendations, steering away from ads targeting shoppers prone to returning products. For instance, Acorn-i, an e-commerce agency working with Perry Ellis, has utilized AI sentiment-analysis tools to optimize product descriptions. This involved identifying phrases that might lead to confusion over elements like size or fit, leading to returns. Generative AI then crafted descriptions aligned with shoppers’ questions and concerns.
In a unique application, Dutch online apparel store Omoda collaborated with Google and DEPT to develop a machine-learning system aimed at reducing return rates from sales generated by search ads. This system combines return rates for specific products with predictive algorithms, assessing the likelihood of customer groups returning their purchases. The data is then fed into Google’s ad-buying algorithm to precisely target search ads, factoring in the predicted cost of returns.
The implementation of AI strategies has yielded positive outcomes. Omoda reported a 5% reduction in returns and a 16% increase in profits for sales driven by search ads since May. Perry Ellis, working with Acorn-i, witnessed a 15% decline in return rates for products in the project over the course of approximately a year.
While retailers have explored AI for managing returns, the technology is still evolving and not yet mature enough to address these challenges on a large scale. Despite the complexities, the growing addiction to returns necessitates innovative solutions. AI, fueled by consumer data, emerges as a promising avenue for retailers to navigate this intricate landscape.
In the absence of a simple solution, retailers are compelled to experiment with AI, leveraging their consumer data to strike a balance between providing a flexible returns policy and optimizing their bottom line. As the retail industry continues to evolve, the role of AI in managing returns is poised to become increasingly pivotal.
Yours truly,
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The Refunds Blog 