Using AI for Critical Returns Management

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.

Artificial 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 first place. AI is not a one-size-fits-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 applications that employees can use on desktops or handheld devices. They help workers 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 efficient way possible, at the highest recovery possible. They do this by analyzing a host of pertinent factors, including product condition, resale price, processing costs, number of touchpoints required, transportation fees, and storage requirements.

By simply scanning a product, AI disposition applications factor every returns management cost, telling workers precisely how to handle the product. Disposition paths include return to vendor, secondary marketplace listing, refurbishment, liquidation, parts harvesting, and recycling.

Disposition applications can help cut processing time by 75%, exponentially increasing their ability to recover higher profits 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 critical information, like how an item will fit.

Images and descriptions are simply not enough, which is why consumers engage in widespread bracketing—buying 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 inability to try the clothes first. Yet only 7% have used virtual fitting rooms. That leaves a significant opportunity for omnichannel brands to find partners or develop their own AI-based dressing rooms.

Recommendation Engines

Before virtual fitting rooms, retailers could employ automated suggestions during the browsing phase of the shopping journey. Known as recommendation engines, these AI and machine learning tools instantly suggest products that align with the person’s preferences. These engines work by building customer profiles 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 databases, consumer profiles, 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 traffic to their omnichannel storefronts. It’s a no-brainer—literally.