Merchandise return transactions are a critical part of the customer experience of retail. The objective is an optimal return rate, where the retailer finds the right balance between too many returns that may lead to fraud and abuse, and too few returns that may lead to customer unhappiness. But how does one arrive at such a measure?
A major retailer recently asked the same question. As a customer of The Retail Equation (TRE), the retailer asked for help in understanding the impact return policies have on business. The retailer, who was experiencing declining sales, needed to pinpoint areas where it might improve, and one of the areas to be evaluated was the return counter.
With return authorization solutions deployed in more than 34,000 retail locations, TRE evaluates an unprecedented amount of return data and, in turn, is able to provide its customers with powerful insights to help them understand and improve their business. Therefore, TRE set forth on a benchmark study to examine the impact different return policies could have on net sales and return rate.
The Methodology
Stores were divided into three groups. The first group, which made up approximately one-fourth of the customer’s stores, was placed into a “strict return rules” test group, designated as the “Strict Group.”
The second store group of similar size began testing The Retail Equation’s Verify return authorization solution–a “friendlier return experience”–at the same time as the first group underwent its strict return rules policy. This second group was designated the “Friendly Group.”
The third or control group operated as usual and was designated as the “Balance of Chain Group” or “BOC Group.”
The Strict Group and the Friendly Group were the most easily compared since they rolled out their new return procedures at the same time. And the results speak for themselves.
Over the course of the test, which ran for six months, the Strict Group showed an 11.2 percent decrease in net sales, while the BOC stores showed a 6.4 percent decrease in net sales. And the Friendly Group showed only a 2.6 percent decrease in net sales–this is an 8.6 percent improvement over the Strict Group.
Taking a closer look, the customer evaluated the net sales trend by store group, comparing the time in the months leading up to the deployment date with the months following. During that time, the Friendly Group showed a minimal change. The net sales decrease leading up to the deployment date was 0.1 percent, while after the deploy date it was 0.2 percent. However, the net sales decrease in the stores where a strict policy was implemented had a 0.6 percent net sales decrease leading up to the change, while a whopping 2.1 percent decrease in the months after they adopted the strict return rules policy.
TRE dug deeper to determine whether these trends differed based on region, and the decline in the Strict Group was universal and widespread across all tested regions.
Return Rate Impact
TRE also evaluated the impact of stricter return policies versus friendlier return policies on return rate. Based on the findings, the Friendly Group and Strict Group both had better return rate reductions than the Balance of Chain Group. The Friendly Group exhibited return-rate reductions due to TR’Õs targeted predictive-modeling approach that only impacts a small portion of all returns–typically less than 2 percent. The Strict Group exhibited return-rate reductions using more punitive rules and policies, for example, by declining all non-receipted returns and directly impacting the overall consumer experience of every shopper.
Strict Return Rules Negatively Affect Stores and Consumers
At the end of the six-month test period, the Strict Group of stores showed an 8.6 percent reduction in net sales compared to sales from the Friendly Group. Stated a different way, if sales declined in the entire chain by 8.6 percent and the overall return rate was 7.2 percent, the retailer would have to reduce their total return dollars by approximately 118 percent to offset the loss in revenue–which is impossible because the total dollars of lost sales would have already exceeded the total return dollars for the retailer.
This shows a real revenue impact and telling statistics for the retailer, plus a key learning point for other retailers. There were several other important conclusions drawn from the study:
- Stricter return rules and policies negatively impact all shoppers, even those who never make a return, because return policies are typically a consideration on every purchase.
- Stricter return rules and policies significantly harm net sales. As mentioned above, the Strict Group of stores showed almost a 9 percent reduction in net sales as compared to the Friendly Group.
- Depending on chain size, the unintended consequence of stricter return rules and policies can cause a revenue decline of hundreds of millions or even billions of dollars.
- Stricter return rules and policies do not lead to any better return-rate reductions when compared to The Retail EquationÕs Verify solution.
About The Retail Equation
Headquartered in Irvine, California, The Retail Equation optimizes retailers’ revenue and margin by shaping behavior in every customer transaction. The company’s solutions use predictive analytics to turn each individual shopper visit into a more profitable experience. This yields immediate financial payback, increasing store comps by as much as 2 percent, with significant return on investment.
Their Software-as-a-Service applications operate in more than 34,000 stores in North America, supporting a diverse retail base of specialty apparel, footwear, hard goods, department, big box, auto parts, and more. For more information, visit TheRetailEquation.com.
TABLE: Return Rate Comparison
Group Prior Year Current Year % Change
Strict stores 7.8% 7.2% -7.7%
Friendly stores 6.4% 5.9% -7.8%
Control stores 7.3% 7.4% 2.0%
