Product returns cost retailers a fortune. Each return negatively affects a retailer’s bottom line in three different ways. First, it is a lost sale. Second, it takes staff and other resources to process and ship the product in reverse logistics, and finally, to decide what to do with the returned product, it involves complex, manual, costly processes for many retailers, which may lead to significant amount of waste . It is not difficult to understand how these three losses can result in considerable costs for a retailer. In 2018 and 2019, total merchandise returns account for nearly $369 and $309 billion in lost sales for US retailers, respectively , . Optoro  estimates that returns cost retailers $400B each year in the US alone.

The current trend in return volume shows that this problem is going to escalate even further. According to the National Retail Federation (NRF), Americans returned $260 billion in merchandise last year (2020), a 66 percent increase from five years ago, and a quarter of that was during the holiday season . The problem is even worse in e-commerce, where at least 30% of all products are returned as compared to 8.89% in brick-and-mortar stores , and this has been even further intensified by the COVID-19 pandemic while shifting purchases toward e-commerce.

Therefore, the huge economic impacts of product returns on the one hand, and their increasing trends on the other hand vitally necessitate careful investigation and outlining of suitable strategies for managing the product returns, especially in the e-commerce market. In this short article, we briefly discuss the results of a research conducted at the University of Massachusetts Boston about the impact of different factors on the optimum pricing and refund strategies that can result in the maximum retailers’ expected profit. The strategies have been devised for a monopolist retailer which faces a heterogeneous market base, including customers that differently value a retail product.

Optimize Your Refund Level

While the full refund policy is a common practice in nowadays markets, yet some retailers hold partial refund policies or charge some restocking fees. Our results show that there are specific circumstances that the benefit of partial refund over the full refund is negligible, or even the full refund is the optimal policy. Therefore, in such situations, the retailer can safely hold a full refund policy in correspondence with the prevalent market expectation. These conditions include the situations when customers have quite similar valuations of the product, combined with a high product salvage value and a burdensome return process. Otherwise, using a partial refund strategy will be more profitable for the retailer.  This is particularly more pronounced for low profit margin products.

Invest on Information Provision

Customers’ uncertainty about the true value of the product can be considered as the main source of product returns. Retailers can alleviate this uncertainty by providing more information about the product. There are various methods and means that the retailers may use to this end; brochures and catalogs are prevalent classic tools, while presentation tracks, discussion forums, comparison tools, and simulation software are the trending ones. To shed light on information provision strategies, we examine two general strategies: maximal and moderate. In a maximal information provision, the retailer aims to completely resolve customers’ uncertainty by providing as much information as possible. In this ideal situation, which might be quite challenging or impossible, customers could accurately evaluate the value of the product prior to a purchase. In a moderate information provision, information is provided conventionally but would not resolve all sources of uncertainty. Our results show that maximal information provision is preferable under most circumstances, particularly for low profit margin products and under high market uncertainty, given the cost of providing the information is not too high. However, when the return process is burdensome or costly for the customers, a moderate information provision might be more profitable for the retailer.

Adjust Your Return Leniency

To ease the hassle of return for customers, many retailers implement some measures of return leniency, which in turn increase the frequency and applicability of the product returns. This trend is being further intensified by retailers adapting new policies during the COVID-19 pandemic that make product return even easier , . Our research findings show that excessive levels of return leniency where customers can return the product with almost no hassle, has negative impact on retailers’ profits, even when the salvage value of the returned product is high. This is due to the fact that the demand stimulating impact of high return leniency will not overweigh the expenses of relatively higher return frequency. Therefore, the best strategy is to have some moderate return leniency when retailers follow the traditional full (or high level of) refund.

Strategize in Line with Product Salvage Value

The high expenses of reverse supply chains often result in overall low salvage values for the returned products. So, on average, the salvage value of a typical product is about 40% of the original selling price , . There are industries and businesses that the salvage value of the returned products is even less than this. In such cases, our results show that the market uncertainty regarding the product valuation plays a key role in devising the return strategies. Note that this factor has a direct relation with the return frequency in the market.

• High product valuation uncertainty: High uncertainty level along with very low salvage values necessitates a partial refund level adjusted in a way that customers would return the product scarcely. Clearly, the return option is only illusory in this case and the leniency level of the return process for the customers does not matter for either the retailer or customers. For high profit margin products, lower partial refund levels will be required in these circumstances.
  

• Low product valuation uncertainty: The strategies and outcomes are pretty much the same if the product is not costly, i.e. high profit margin products, while a relatively higher refund level would be necessary compared to the case of high uncertainty. However, for the low profit margin products, this is mostly the product price that helps the retailer deter the returns. This means setting a high price by the retailer such that only customers who highly value the product would purchase and never return it. If this is the case, the retailer is able to safely offer full refund level, and return leniency again does not impact either the customer behavior or retailer gain.

The decision regarding the information provision depends mostly on the cost of acquiring and selling the product. Our results show that for more costly product, moving toward a maximal information provision can be more profitable for retailer.

Make Use of Endowment Effect and Its Underestimation

Endowment effect refers to the tendency of a person to feel a higher valuation for a product after the purchase just because now the person owns the item. Based on this behavior, people tend to keep the ownership of the purchased product even when their actual valuation of the product is less than what they expected. Moreover, customers usually underestimate the magnitude of the endowment effect and its impact on their ex-post valuations. Both endowment effect and its underestimation can benefit the retailers as they lead the customers to buy the product with assumption that they would return it if they found it unworthy of the purchasing price, and then find themselves attached to the product more than what they expected, which prevents them from returning it.   

Several factors affect the level of endowment effect and its underestimation. For instance, they can increase as the length of ownership increases. They might be even different for the same person at different times. There are also situations that the endowment effect and its underestimation can be negligible. For example, when the customer is an organization rather than a person, or when the product is highly technical and in-adaptability with the usage environment renders the product useless. There are many other situations that the endowment effect and its underestimation plays an important role in the purchasing and returning decisions. For instance, most of the products purchased online by families are included in this category, especially when the buyer is young and excited about the purchase.

For high levels of endowment effect and its underestimation, our research results show:

• The retailer can safely offer a full refund policy, even when it is not the optimal refund level, while sacrificing only a negligible amount of profit.
  

• Moderate information provision would be the best strategy only when the cost of acquiring the product is high and return process is challenging for the customers.
  

• It is beneficial for the retailer to provide greater return leniency, especially if the uncertainty is high or the cost of acquiring and selling the product is low. This implies that the endowment effect and underestimation work well as demand stimulating and return deterrent factors, which means the retailer experiences fewer returns and their subsequent losses.
  

• The product salvage value is less influential in the optimal price and refund levels since the return frequency is negligible.

Beware of Loss Aversion

Loss aversion is another major factor affecting the return decisions. According to a loss averse decision maker, losses loom larger than gains. This behavior intensifies the potential disutility of buying a product that is less valuable than the paid price. Our research results show that unlike the endowment effect that can benefit the retailers by diminishing returns, loss aversion can always hurt their bottom line. Customers’ loss aversion can have an increasing effect on optimal refund level, particularly when the market uncertainty and cost of acquiring the product are high.

On the other hand, customers’ loss aversion could be beneficial for retailers when they target a full refund policy while the optimal refund level is lower than that.
While the results presented in this article could be helpful as a general guideline for the retailers to approach their product return challenges, there are other business-specific factors that should be taken into consideration. More detailed measurements, modeling, and analysis of all these factors, along with those mentioned in the article, are needed before a quantitatively optimized strategy could be prescribed.

SOURCES
¹ Creating Value from Returns this Holiday Season
  https://rla.org/media/article/view?id=1179
² 2018 Consumer Returns in the Retail Industry
  https://appriss.com/retail/wp-content/uploads/sites/4/2018/12/AR3018_2018-Customer-Returns-in-the-Retail-Industry_Digital.pdf
³ Consumer Returns in the Retail Industry 2019
   https://appriss.com/retail/wp-content/uploads/sites/4/2020/01/AR3019-2019-Customer-Returns-in-the-Retail-Industry.pdf
⁴ www.optoro.com
⁵ Creating Value from Returns this Holiday Season
   https://rla.org/media/article/view?id=1179
⁶ 5 Things Retailers Must Understand About Product Returns
   https://www.mytotalretail.com/article/5-things-retailers-must-understand-about-product-returns/
⁷ E-commerce Product Return Rate – Statistics and Trends [Infographic]
  https://www.invespcro.com/blog/ecommerce-product-return-rate-statistics/
⁸ The pandemic has pushed more sales online, and that means more returns, too
   https://www.post-gazette.com/business/money/2020/09/14/retail-shopping-covid-19-pandemic-sales-online-in-store-returns/stories/202009130028
⁹ Roggio, A. (2020). Pandemic Alters Retail Return Policies. Retrieved from https://www.practicalecommerce.com/pandemic-alters-retail-return-policies
¹⁰ Thomas, L. (2020). Retailers face another challenge during coronavirus: Handling returns. Retrieved from https://www.cnbc.com/2020/04/14/coronavirus-dealing-with-returns-could-be-bigger-burden-for-retailers.html
¹¹ Return Season Hits Hard
    https://www.pymnts.com/newes/retail/2020/return-season-hits-hard-some-retailers-see-bright-side-of-customer-experience/
¹²  U.S. Industrial & Logistics ViewPoint: Reverse Logistics Stress Test 2020
     https://www.cbre.us/research-and-reports/US-Industrial--Logistics-ViewPoint-Reverse-Logistics-Stress-Test--2020
¹³ Kahneman, D., & Tversky, A. (1977). Prospect Theory. An Analysis of Decision Making Under Risk.
¹⁴ Choice, R. (1984). Choices, Values, and Frames.

 

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Ali Shirzadeh Chaleshtari and Ehsan Elahi