Product returns in retail markets have huge economic impacts, as each return is a lost sale. Besides, there is the cost of processing returns, in terms of staff and resources. In addition, determining what to do with a large volume of returned products is a complex process, and many retailers rely on manual, rule-based decision-making and inefficient processing, which can lead to a significant amount of waste1. In 2018 and 2019, total merchandise returns account for nearly $369 and $309 billion in lost sales for US retailers, respectively2,3. Optoro4 estimates that returns cost retailers $400B each year in the US alone5. Adverse environmental impacts of the returns are another problem, as not all returns can be successfully discounted and put back in the rotation. Optoro estimates that returns produce 5 billion pounds of waste in landfills annually6. Furthermore, the United States generates 15 million tons of carbon emissions every year due to product returns7.

The increasing trend of return frequency is noticeable as well. 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 season8. 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 stores9, and this has been even further intensified by the COVID-19 pandemic while shifting purchases toward e-commerce10. These all necessitate careful investigation and outline of strategies to manage the returns, especially over the e-commerce market.

Aside from the opportunistic returns where customers intend to return the product since the time of purchase, a major root cause of returns is customers’ uncertainty regarding the products fit for their needs or products’ actual values for them. This can happen more often than not in online shopping of highly featured products, or when the product should match other items, such as decorative products or pieces of clothing, or simply when the customer wants to buy a new type of product or brand. Customers’ uncertainty can be alleviated via information regarding the product, which is provided online in classic formats such as videos or descriptions, or trending fashions such as comparison tools or simulation software.

Therefore, information provision practice can benefit both customers and retailers via decreasing return frequency. However, from a profit point of view, maximal information provision may not be beneficial for the retailer, as this practice may cut down the market demand. With maximal information provision, the retailer may lose customers who otherwise would find the purchased product not worth its price, but would fail to return the product because of the hassle of return or missing the opportunity for other reasons. In such cases, retailers also benefit from the psychological phenomenon of the endowment effect, which tempts customers to keep ownership of the product even if the product is not to their satisfaction. All in all, when we add the cost of providing information, the maximal practice of providing information may become even harmful to the retailer. In those cases, the maximal practice of information provision is not justifiable from a social point of view either, considering the subsequent losses to the retailers as social entities.

Therefore, two major strategies of information provision practices are usually considered to manage product returns, maximal practices that aim to resolve any sources of uncertainty for customers, so that customers would quite accurately understand the product value and fit prior purchase; and moderate practices, as opposed to maximal, where information is provided conventionally but would not resolve all sources of uncertainty. In the following, we briefly present the applicability of each of these strategies for a monopolist e-commerce retailer under various market conditions, based on the results of research conducted in the Department of Management Science and Information Systems at the University of Massachusetts Boston.

It is interesting to know that the endowment effect has a determinant role in outlining the best strategy of information provided under different conditions, as the optimal practice depends on the magnitude of this factor. Imagine the circumstances with negligible endowment effect, first. Various cases may correspond with such conditions. 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. In such conditions, a maximal information provision strategy is most beneficial. An exception to this recommendation happens when returning the product is difficult or costly. For instance, if the product in question is liquid or a flammable item, such as alcohol or detergent, customers may not be able to return it by mail, and return may be possible only in certain physical stores. In such cases, the retailer could benefit from the moderate information provision strategy. But other than these exceptions, maximal information provision strategy is optimal for all other cases when the endowment effect is negligible. This is more pronounced when the product is costly and market uncertainty regarding the product is high. Imagine a luxurious wireless phone charger for cars. The product should have a high cost of acquiring for its retailer due to the technological novelty and innovative design. Besides, because of the technological constraints, the product should not be adaptable with every phone and every car, which means more uncertainty. Moreover, in case of inadaptability, customers may not experience any endowment effect after the purchase since the product would be useless for them. For such a product, maximal information provision from the e-commerce retailer to the market benefits both retailer and customers.

Now let us imagine the cases with a considerable amount of endowment effect. Most of the products purchased online by families are included in this category, especially when the buyer is young and excited about the purchase. In such cases, when the cost of acquiring the product is high and the return process is challenging for the customers, moderate information provision would be the best strategy. Otherwise, maximal information provision is again the recommended strategy.

In our future articles, we will discuss how the optimal pricing strategies of e-commerce retailers are affected by their information provision strategies.

^{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 Leading retailers and brands use Optoro to revolutionize how they process, manage and sell returned and excess inventory. 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 Research and Markets – The World’s Largest Market Research Store. https://www.researchandmarkets.com/reports/4911530/the-environmental-impact-of-e-commerce-2020?utm_source=dynamic&utm_medium=GNOM&utm_code=xmpm26&utm_campaign=1344501+-+2020+Report+on+the+Environmental+Impact+of+E-Commerce&utm_exec=joca220gnomd
8 5 Things Retailers Must Understand About Product Returns https://www.mytotalretail.com/article/5-things-retailers-must-understand-about-product-returns/
9 E-commerce Product Return Rate – Statistics and Trends [Infographic] https://www.invespcro.com/blog/ecommerce-product-return-rate-statistics/
10 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}

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Dr. Ali Shirzadeh Chaleshtari & Dr. Ehsan Elahi
Dr Ali Shirzadeh Chaleshtari is a Research Scholar at University of Massachusetts Boston, and formerly, an Assistant Professor of Azad University. His areas of expertise include project scheduling, supply chain management, product pricing and return management. He acquired his PhD in Industrial Engineering from Sharif University of Technology. Dr Ehsan Elahi is an Associate Professor of Management Science at University of Massachusetts Boston. His areas of expertise include Supply Chain Management, Behavioral Operations Management, Operational Risk Management. He has a PhD in Industrial and Systems Engineering from the University of Minnesota. Drs. Elahi and Shirzadeh Chaleshtari work on research projects on managing product returns in retail markets, aiming at devising optimal pricing, refund, and marketing strategies to manage the returns.