More than $761 billion worth of online shopping items have been returned in the United States! This is the category with the highest return rate

It is learned that according to a survey by the National Retail Federation and ApprissRetail, the online shopping return rate in the United States jumped from 10.6% a year ago to an average of 16.6% in 2021, and more than US$761 billion worth of goods are expected to be returned.

According to NRF data, online sales accounted for about 23% of the $4.583 trillion in total U.S. retail sales in 2021. As consumers bought more goods online during the pandemic, return rates also increased. These returned goods forced retailers to bear losses.

NRF also expects higher holiday return rates. The survey found that on average, retailers expect 17.8%, or $158 billion, of merchandise sold in November and December to be returned. It is reported that holiday sales in 2021 increased 14.1% year-on-year to a record $886.7 billion.

Certain categories have higher return rates than others. According to this year’s survey, auto parts had the highest return rate, averaging 19.4%. This was followed by apparel, averaging 12.2%, and home decor and household goods, averaging 11.5%.

For some retailers, the high costs of returns have led to new measures. Walmart acquired virtual fitting room startup Zeekit for an undisclosed price; Best Buy has an online store that sells unboxed appliances, TVs and other products that are covered by warranty; and some other companies, including Amazon, are offering refunds without returns.

However, this can encourage consumers to overspend, such as buying a dress in multiple colors and sizes. Returns can also create opportunities for fraud. The NRF found that for every $100 in returned merchandise accepted, retailers lose $10.30 to fraud.

This will add to the challenges facing merchants facing supply chain problems, so some savvy retailers are increasingly focusing on return and trial strategies, using 3-D images and artificial intelligence-driven tools that can recommend appropriate sizes to minimize return rates.

Editor ✎ Xiao Zhu/

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