What Is the Optimal Retail Return Rate?
Accepting retail returns began as a marketing technique. The objective, then and now, is to give the consumer the confidence to trade that hard-earned paycheck for something new. A trustworthy retailer, the reasoning goes, stands behind the quality of its merchandise. By building the confidence to purchase, the retailer successfully develops a loyal customer base and invites new customers as well. Ecommerce sites are even more dependent on establishing trust, and this could be why they are encouraging shoppers to keep buying and returning until they find the right fit or style.
The business question then becomes, how much confidence can a retailer afford? Several retailers have been noted for their extremely generous retail return policies. A great many other retailers fall into a 14- to 30-day return period. A few do not allow returns.
Does that mean that choosing the most-common timespan leads to the best retail return rate? It’s not that easy.
The optimal return rate strikes the right balance between a situation that permits too many returns that lead to net losses or fraud, and a situation that allows so few returns that it limits consumers’ desire to make purchases, leading to lower revenue.
The return rate is influenced by many factors. In order to optimize your return rate, you need to optimize your returns process. Ask the following questions:
- How is the consumer return experience at your store?
- What are your current return policies?
- Do your return policies create friction your best customers?
- Are your return policies different by channel?
- Do you offer Buy-Online-Return-In-Store (BORIS) to your ecommerce customers?
- How does the BORIS process differ from a return of goods purchased in a store?
- Do your practices deter only fraudulent returns?
- What actions and overrides are being performed during the return process?
- Do you have a method for validating receipts at the point of return?
- How do associates feel about the return process at your store?
- What is your current consumer conversion rate?
- How are good consumers being rewarded for their patronage?
- Does anyone review “good customer” returns?
- Do you incorporate the return transaction into your reward system?
- Do returns factor into your current process that helps “average customers” become “best customers?”
Your answers will reveal areas for potential improvement. For example, in an ecommerce transaction the consumer’s credit card is charged when the item is shipped, not when it is delivered. Do you , therefore, need to allow more time for BORIS returns in order to compensate for the delay?
The role of policies
Many retailers depend on crafting policies to manage returns. Have you reviewed your policies in the last two years or since introducing new channels or services? Changes in consumer shipping channel, store environment, consumer demographics, or economic conditions can all cause your return policies to become obsolete.
Research by three data scientists here at Appriss Retail found that a very strict return policy caused consumers to limit their purchases. This resulted an almost nine percent reduction in net sales compared to stores with a “friendly” return policy. Depending on chain size, this could be a loss of hundreds of millions or even billions in sales. Rather than protect profits, strict policies diminished profit.
By revisiting your policies, you have a real opportunity to put more on the bottom line and increase customer satisfaction. Then you can take it one step further.
Optimizing returns using analytics
The simple receipt verification offered within a point-of-sale solution is not adequate. A retail Return Optimization solution examines consumer data, trends, purchase history, and products in order to treat individual consumers in such a way that they are most profitable to the company. Some examples are:
- Allowing top-tier loyalty customers extra time to make returns
- Issuing incentives for high-value customers to stay in the store and shop for a replacement for returned items rather than leave the store empty handed.
- Warning extreme, serial returners that the store has the right to restrict their return privileges if their return rate gets too high.
The fact is, about 80 percent of consumers do not make returns. They do, however, consider your returns policy before making a purchase, simply as a form of insurance. Of the remaining 20 percent, only about one percent are extreme returners. Why subject all consumers to policies meant to restrict that one percent? A better alternative is to determine the optimal return rate and focus on using return policies to attract consumers and develop their loyalty.
Return optimization is a process that tracks purchase and return histories and combines them with statistical models to discourage fraudulent and abusive return behavior, while at the same time encouraging good consumers to continue their shopping experience by issuing custom incentives.
By using a statistical modeling approach, you can more accurately determine true return fraud, making legitimate returns fast and easy while eliminating abusive returns. The same type of statistical algorithms that have transformed Price Management, Return Optimization is now available to identify which consumers to reward for their patronage during the return process and which consumers are abusing services and/or defrauding the business.
Store associates appreciate Return Optimization because they can deliver good service to good consumers but let the system choose an appropriate action for abusive or fraudulent returns. No one wants to be the “bad guy” who must tell a consumer that a return won’t be accepted. Being able to attribute that responsibility to “the system” makes their job more pleasant (and more accurate). Likewise when “the system” can offer rewards to best customers, the associate does not have to worry about being accused of bias. His or her sole focus can be on providing a positive experience. Transferring responsibility for returns to an outside third party is a behavioral advancement similar to the Check Verification Systems that were created in the 1990s.
Retailers in the U. S. leave $369 billion on the table when consumers leave the store without making a purchase after a return. Returns present a quantifiable and attributable resource for new sales.
“If you have not focused on managing return dollars as a method of protecting your profits, then you may be leaving money on the table…it was the highest and quickest capital ROI in the company.” –Director, Asset Protection for an apparel retailer.
Retailers have a real opportunity to put more on the bottom line AND increase customer satisfaction. Instead of just trying to manage returns, there is a better way to minimize fraudulent returns and consumer frustration, while maximizing return transactions’ untapped value—Return Optimization.