Pushing Back Against Returns Abuse

Most of us didn’t know it at the time, but 2005 marked a turning point in retail. That was the year free two-day shipping arrived on the scene. Customers loved it then, and they still love it now. In fact, customers loved it so much that free shipping — for both the initial delivery of the goods and any necessary returns — became an industry norm. Eighty-five percent of customers now expect free returns.

But free shipping isn’t really free. It’s a significant cost that customers used to shoulder and retailers now absorb. They’re happy to absorb that cost when it leads to more sales and revenue, but when indecisive or unscrupulous customers start to take advantage of generous return policies, retailers risk major losses. How do smart retailers curb their losses while continuing to offer a policy that has become a customer expectation? By intelligently applying technology to recognize and stop abusers while enabling legitimate customers.

The Problem

One of e-commerce’s greatest blessings is also a curse: consumers can get nearly anything delivered anywhere, but they can’t touch and feel the items until they’re received. Many consumers purchase clothing in multiple sizes and styles, knowing they can easily return the unwanted items later. Free return policies are designed for that, but it can get very costly when customers return substantially more than they keep.

Even more threatening are the customers who are borrowing more than actually shopping. These customers might want special or expensive items to wear once for an event such as a wedding, date or job interview. More recently, retailers have to contend with shoppers who buy items for the social media cachet. They get the goods, snap and share a photo of them, and then return them to the retailer.

The Costs

When customers return goods, retailers face a multitude of costs. With free returns they are, of course, paying to get their goods back, but that’s not all. They may take a hit when products purchased at full price have since been discounted. They may have to sell the returned products as “open stock” for a discount. And some products can’t be restocked at all. These costs add up and can eat into profits.

Such behavior costs U.S. retailers an estimated $23 billion a year. One major retailer discontinued its lifetime returns policy after customers rang up $50 million per year in “worthless returns” amounting to roughly 30 percent of its annual profits. Other retailers have taken to banning shoppers for excessive returns and deactivating customer accounts.

However, free returns are a competitive advantage. Eliminating them may alienate customers for whom free returns are part of the draw to online shopping. These spurned customers may go to competitors and never return, costing retailers lifetime value.

Worse still, the “solution” of blocking return abusers often doesn’t work. Abusers respond by using guest checkout or changing the details of the order to avoid being recognized. “John Doe” becomes “Jonathan Doe” to circumvent the restriction, for example.

That’s where machine learning (ML) can help. ML-based solutions can analyze transaction details to recognize the individual behind a transaction with a high level of accuracy. With the right data, a ML solution can recognize when John Doe is using guest checkout or shopping under another name.

With that accurate identification in hand, retailers can then safely apply their policies in a way that makes financial sense. Free shipping and returns can continue to keep loyal customers coming back as well as give potential customers a reason to make a purchase. Return abusers, on the other hand, may see shipping or restocking fees clearly communicated and applied to their purchases.

By disincentivizing returns abuse, retailers can continue to offer the rest of their customers the benefits to which they’ve become accustomed while curbing its worst excesses. Solutions can even work with multiple retailers to identify a return abuser across shops, giving retailers an early warning that a shopper might be bad news. With this type of accurate identification and a dynamic policy, retailers can have their cake and eat it (or return it), too.

Assaf Feldman is chief technology officer and co-founder of Riskified, an e-commerce revenue protection and fraud prevention solution.