Balancing Customer Experience and Cost During Returns' Biggest Season

The Super Bowl of returns is coming.

Optoro estimates that approximately $173 billion in goods will be returned in the U.S. between Thanksgiving and the end of January 2024. 

This, compounded by a challenging holiday shopping season compared to years past, could hit retailers hard and force them to offer steep discounts. To avoid further revenue hits, retailers need to save sales and reduce returns costs, from shipping to processing —since returns alone can cost retailers about 50% of their margin. 

To prepare for difficult playing conditions, 87% of retailers are rewriting their game day playbooks and revising returns policies. Their biggest challenges are determining how to deliver a smooth and easy returns experience with expectations that are set by the likes of Amazon, engender trust and repeat engagement, and keep costs down. 

Returns technology is an emerging and highly successful strategy that enables retailers to strike a difficult balance by leveraging three power players: preventing fraud, elevating returns and machine learning.

Reward loyalty while blocking fraud and abuse

If returns technology is the star player in the post-holiday retail blitz, then returns fraud is its biggest rival. Returns fraud can take various forms, from wardrobing, in which an item is used and returned for a refund, to claiming a product never arrived, or exaggerating issues for a refund. 

These abuses have a major impact, costing retailers $100 billion a year. Less nefarious behaviors like bracketing — in which a person buys multiple items in various colors or sizes, only to return the ones they don’t like — also take a toll. 

But in a cacophony of returns, how can retailers separate innocuous behaviors from bad actors? Advanced analytics and machine learning can help retailers distinguish between unproblematic customers and return abusers. Fraud and abuse algorithms can identify risky customer behaviors — and limit returns options as a result. 

On the other hand, retailers can reward loyal customers with convenient perks like  home pick-ups or instant repurchase options like exchanges or gift cards, which also allow the retailer to keep the sale. Reducing the propensity of fraud can save retailers big money and simultaneously, rewarding loyalty puts the focus back on growth.

Get elevated returns off the sidelines

Customer expectations around returns are sky high. For retailers to compete, they need to take a more customer-centric approach, creating returns experiences that offer greater flexibility and convenience.

After all, a staggering 95% of shoppers say a poor returns experience will make them less likely to shop from a brand again. Retailers need to embrace customizable return methods. Increasingly popular is the flexibility to drop off unboxed, unlabeled goods at a physical location. 

For added convenience, some retailers, like Gap, also offer to pick up a package straight from a shopper’s doorstep. Brands can choose whether to make these services complimentary, a loyalty perk, or tied to a nominal fee — and it’s worth noting that 70% of shoppers are willing to pay a little extra for a great customer experience.

The added convenience of drop-offs and pick-ups encourages customers to make their return faster, leading to quicker restocks and increasing the likelihood an item will sell again for full price. These methods also allow for consolidated shipping back to warehouses. 

Shipping more returns in fewer boxes lowers shipping costs by up to 20% while significantly reducing emissions. Ultimately, offering convenient return options increases customer satisfaction, boosts loyalty and compels repeat engagements.

Score with machine learning

Just like moving the ball toward the end zone, speed and agility are the name of the game in retail. The faster returned items get back to stock, the more likely they are to get resold for full price — a timeline that’s especially tight for clothing and seasonal items.

Machine learning is the MVP. Advanced algorithms improve processing speeds, by determining returned items’ most efficient and profitable path, whether that’s back to stock, secondary markets, or liquidation.

The smart disposition of goods ensures each item captures the top dollar based on its condition, season, value, and more. This technology also eliminates the guesswork and cost of incorrect routing, drastically reducing the number of touch points, distribution centers, and time to restock for each item.

Machine learning also enables brands to score extra points through data-rich analytics. Once a black box for retailers, returns data offers rich insights to help brands make reverse logistics work for them. Returns technologies allow for itemized visibility, giving retailers the complete picture of their supply chain.

Walmart, for example, uses AI to anticipate demand during peak seasons like the holidays, while American Eagle has embraced AI-powered inventory tracking technology in over 500 of its stores across the U.S.

As the Super Bowl of returns approaches, retailers must be agile, playing a strong game both offensively with elevated e-commerce returns, and defensively by connecting the best supply chain practices. 

Embracing technology enables retailers to stay focused on the long game. That means integrating innovative returns methods to find new ways to lower costs, while giving shoppers competitive experiences that lead to growth opportunities and enable circularity in the supply chain. 2023’s match is going to be a challenge, but with the proper equipment and prep, retailers can win.