Retailers Use AI to Master Holiday Returns 

It’s only early October, but by the looks of current ad campaigns and the décor in retailers’ brick-and-mortar shops, it’s already Thanksgiving. It’s a signal to all that the holidays are approaching, and with them, the onslaught of holiday shopping, both in-store and online. Over the past few years, retailers have enjoyed particularly busy holiday shopping seasons. In fact, holiday sales have been on the rise, increasing each year: In 2020, holiday revenue came in at $783.4 billion; 2021 saw $889.3 billion; and 2022 topped $936.3 billion. Analysts expect similar growth for 2023. However, with heavy sales volume comes a heavy volume of returns. In 2022, retailers saw a 63 percent increase in holiday returns, and that number equaled nearly 18 percent of their total sales.

All this suggests that retailers must be well-prepared for an extra busy and extra costly 2023 season of returns. But never fear, there’s yet another use case for artificial intelligence. Here, we dive deeper into holiday returns challenges and how AI can intervene to scale-up efficiency and overall value of holiday returns.

Challenge No. 1: Return Volume Over an Increasingly Longer Shopping Season 

Remember the good old days when Black Friday was the official first day of holiday shopping? Not anymore. The holiday shopping season seems to start earlier and earlier each year, with a large chunk of consumers kicking off their holiday shopping by or even before Halloween. In addition, in 2022, 41 percent of retailers extended their holiday return period, and 10 percent of orders were returned every week from November 2022 until the end of the year. This extended shopping and return window means complications and return costs are stretched across three quarters instead of just one.

Using AI, retailers can capitalize on the longer holiday window using “smart” routing. Interpreting large datasets in real time, AI makes it possible to route returns where stock needs replenishment, and send items to locations with higher demand, encouraging resale. It can also determine the fastest return route, allowing retailers to enjoy the longest possible resale window.

Challenge No. 2: High Fulfillment Costs

November and December define retail’s peak season, and all fulfillment-related costs — mostly staff and shipping — are at a premium during this time. Additional overhead is required for basic retail operations, so holiday profit margins are thinner even before returns costs like transportation and handling are factored in.

In response, AI can cross-reference real-time shipping and product demand data to understand which returns can still create profit and uncover cost-efficient return paths. This means retailers benefit from using the least expensive return methods, avoiding reclamation costs on items that can’t be resold, and reducing processing time and markdowns.

Challenge No. 3: Increased Operational Stress Due to Labor Shortages 

Holiday shopping crowds may be increasing, but the availability of seasonal help isn’t responding in kind. In fact, 86 percent of retailers that hire seasonal employees struggle with staffing during the holiday season. And with 32 percent of staff spending valuable time processing returns instead of selling, retailers are feeling the pinch in their revenue.

AI helps here by drawing from sales and HR systems data to inform decisions that will prevent understaffed locations from getting too overwhelmed. For example, it can identify which locations are experiencing the highest volume of returns and reroute returns to locations with efficient staffing, then divert unsellable items to nonretail locations.

Challenge No. 4: Spike in Returns Fraud

Returns fraud is a real threat to retailers throughout the holiday season, spiking in activity between Dec. 11 and Dec. 16. In 2022, returns fraud cost retailers $17.3 billion, with buy online, return in-store fraud coming in 48 percent higher than other return paths.

AI can monitor payment logs and consumer behavior to help retailers avoid compounded losses from returns fraud. AI does this by identifying and flagging suspicious activity across channels, routing suspicious returns to nonretail locations, and mitigating wardrobing and bracketing abuses.

Challenge No. 5: Excess Inventory

Having excess inventory is directly related to profit erosion, increasing the need for markdowns and warehousing and carrying costs. Holiday returns exacerbate the problem of excess inventory because the items themselves are more likely to be seasonal. If they’re returned, the retailer must either hold onto them for another year or discard them — after having already paid all the logistical fees associated with the return.

Using AI, retailers can better balance inventory levels by tapping into product and store data to qualify returns and send each return to the most appropriate location. This prevents unnecessary stock accumulation. Seasonality is factored into returns decisions, data is used to establish priority returns locations, and unsellable items are kept away from overstocked stores.

Final Thoughts

As rising costs squeeze margins, retailers have to find some way to tackle the long holiday returns season without losing their proverbial shirts. AI can help retailers ensure that returns won’t force them to start the post-peak season in a rut. By strategically applying AI to returns management, retailers can become more efficient and remain profitable during the holiday season and beyond.

However, retailers can go a step further by looking at returns as an integral part of returns management, rather than as a separate category. When retailers evaluate returns as part of a larger supply chain rather than evaluating each return separately, savings related to markdowns, logistics costs and employee time can be realized.

Stuart Ford is the president and COO at DropIt, an AI plug-in that delivers optimized fulfillment, smarter returns and total inventory visibility.