REVERSELOGISTICS [ INSIGHT ]
by Yuri Yushkov CTO, goTRG marketing@gotrg.com | 855-874-8741
Using AI for Critical Returns Management In 2021, U.S. returns rose to $761 billion annually, up 10.6% from the year before. And with that, retailers must consider AI-based automated returns solutions to manage the influx. used virtual tting rooms. That leaves a signi cant opportunity for omnichannel brands to nd partners or develop their own AI-based dressing rooms.
RECOMMENDATION ENGINES Before virtual tting rooms, retail- ers could employ automated suggestions during the browsing phase of the shop- ping journey. Known as recommendation engines, these AI and machine learn- ing tools instantly suggest products that align with the person’s preferences. These engines work by building customer pro- les using a mix of consumer feedback, purchase history, and returns patterns. Intelligent chatbots are another piece of technology that is imperative for online stores. This AI-based tool uses natural language processing to conduct customer conversations. Intelligent bots can tap into product information data- bases, consumer pro les, and purchase history to answer questions and make informed product recommendations. Retailers can walk through any department and instantly see how AI can optimize their returns management strategy. Investment in AI engines allows organizations to do more with fewer workers while reducing return rates, raising the bar on customer satisfaction, and driving more traf c to their omnichannel storefronts. It’s a no-brainer—literally. n
By simply scanning a product, AI disposition applications factor every returns management cost, telling work- ers precisely how to handle the product. Disposition paths include return to vendor, secondary marketplace listing, refurbishment, liquidation, parts harvest- ing, and recycling. Disposition applications can help cut processing time by 75%, exponentially increasing their ability to recover higher pro ts from secondhand goods. VIRTUAL FITTING ROOMS In 2021, online purchases grew by 61% YoY, and consumers returned $218 billion of those orders. One main reason is that consumers struggle to make appropriate purchase decisions online because they lack access to crit- ical information, like how an item will t. Images and descriptions are simply not enough, which is why consumers engage in widespread bracketing—buy- ing more than one variation of the same item with the intent to return at least one. Online shoppers say that 73% of their apparel returns are caused by their inabil- ity to try the clothes rst. Yet only 7% have
Arti cial intelligence (AI) can tackle every aspect of returns processing, from shipping to disposition decisions to recommerce. It can also handle parts of the customer journey to prevent returns from occurring in the rst place. AI is not a one-size- ts-all approach, but can be tailored to every organization. AI disposition engines are one of the most crucial tools retailers can utilize in returns management. Retailers that employ this data-driven technology as early in the returns process as possible will see the most profound results. AI disposition engines are applica- tions that employees can use on desktops or handheld devices. They help work- ers make lightning-fast returns allocation decisions based on real-time data across millions of products. In short, these applications move returns back into the supply chain as quickly as possible, in the most ef cient way possible, at the highest recovery pos- sible. They do this by analyzing a host of pertinent factors, including product condition, resale price, processing costs, number of touchpoints required, trans- portation fees, and storage requirements.
58 Inbound Logistics • July 2022
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