Finding Untapped Value in Product Returns
The little-known, but dark secret of e-commerce is this: On average, for every three packages a retailer ships, one will be an eventual return. Retailers must shift their focus to processing the continuously increasing number of returns.
As retail grows more reliant on analytics, retailers are seeing previously untapped value in data about who is returning merchandise, and why.
Some frequent returners are also frequent purchasers, finds research by J. Andrew Petersen of the University of North Carolina at Chapel Hill and V. Kumar of Georgia State University. The pattern can look like this: A customer buys three sizes or colors, keeps one, and returns the others at no cost. The customer continues to purchase from the site, generating profits that more than make up for returns costs.
Identifying and marketing to those customers pays off. In the academics’ study, profits shot up by more than 45 percent per customer, on average, in the six-month window studied, and by 29 percent over three years, when a retailer used the researchers’ proposed blend of marketing and liberal returns policies to reach out to customers who return a lot, but buy even more.
Many returns, however, are preventable, caused by shipping errors, poor product descriptions, or manufacturing quality issues.
Preventable returns cost retailers $642.6 billion each year, according to retail analyst firm IHL Group, which finds that:
- 22 percent of preventable returns are due to the differences between how the product looked online and in person.
- 23 percent of preventable returns are due to the retailer shipping the wrong item.
Retailers that collect high-quality data and discover insights about returned products can act quickly to correct problems. For example, after learning that a case of red scarves was placed in a warehouse slot where the green scarves were supposed to go, a retailer can act immediately to stop additional mis-shipments.
To get better data on returns, here are three steps retailers can take:
1. Rewrite reason codes to better reflect the issues customers typically report. One key to better insight is adding a comment box to returns forms so customers can volunteer details beyond the typical reason codes. Also, avoid including a prepaid shipping label and return form in the box as precious time-sensitive data can get lost in back-end reverse logistics processes.
2. Inspect returned products. Examining at least a percentage of products that come back, and noting problems, can help receivers identify otherwise undetected problems. If you manage your own returns, not having a method of data collection at the point of return is a missed opportunity to gain additional data.
3. Apply returns analytics to spot patterns. Real-time processing of returns data can quickly identify negative product patterns and deliver valuable insights into real customer experiences with products. This step requires access to the aforementioned data, along with the technical expertise to set up a system for returns prevention and visibility into the drivers of returns.
Armed with good data, retailers increase the odds of eliminating preventable returns, delighting customers, and keeping them coming back for more. With preventable returns out of the way, retailers can focus on the good kind of returns—the ones that drive larger profits.