Not All Returns Are Bad: How Retailers Can Reduce Unnecessary Returns

It comes as no surprise to retailers that returns are rising across nearly all product categories. Increases in online shopping, general expectations for free shipping, and consumer conditioning around style sampling (a la Stitch Fix, Rent the Runway, and others) are all contributing to increased rates of return in aggregate. In 2018 alone, U.S. consumers returned more than $369 billion of unwanted merchandise. Not only does this cause significant expense to retailers because of shipping, warehouse and out-of-stock costs, but unnecessary returns also have a significant environmental impact and negatively affect many brands’ sustainability efforts.

Despite the industrywide challenge of returns, the solution isn’t simple. Policy chances to reduce incentives like free returns across the board can be far more damaging. The key to approaching returns is to understand customer behavior.

Differentiating Returns Based on Behavior

A retailer’s best customers, or its Super Shoppers, can also be repeat offenders when it comes to returns. Super Shoppers are individuals who make purchases frequently, buying a majority of items, from toilet paper to groceries to clothing, online. These shoppers account for two-thirds of consumer spending online, equaling $372.5 billion annually. They are by far the most profitable customers, with net sales 3.6 times higher than the average shopper. An important behavior of Super Shoppers is that they try more products and styles before they buy or keep. This means Super Shoppers might make more returns, but they also keep 25 percent more than the average customer.

Serial Returners on the other hand are a challenge to retailers. They're usually serial spenders who take advantage of free returns on a repeated basis without keeping a significant amount of product. This can be because of size uncertainty, style uncertainty, social media, etc.

The key here is to use customer data to analyze repeat consumer behaviors and spot if the return is valuable (from a Super Shopper) or unnecessary and preventable (for instance from issues with fit). Then analyze that data to see if a modification of returns policy is required.

Moosejaw Cuts Size Sampling

Moosejaw is one example of a retailer that took a proactive and data-based approach to cutting unnecessary returns. The company noticed that approximately 15 percent of its returned merchandise from online sales could be attributed to a behavior called “size sampling,” where a shopper isn’t sure of their size, so they purchase two with the direct intention of returning the one that doesn’t fit. For example, a woman who is ordering a Patagonia dress might be between a size medium and large, ultimately ordering both and returning the large.

Size sampling is problematic for both Moosejaw, which was then faced with additional costs of having the large size out of stock, then restocking, plus inconvenience and time wasted on the customer's behalf. It’s possible that next time this shopper considers an item, she might not want to go through the process of a return and simply won't make the purchase.

To solve this issue, Moosejaw worked with True Fit to implement a pop-up recommendation when a shopper has two of the same product of different sizes in their basket. By prompting the shopper to answer a few questions about their usual size in another brand, or details about their height, weight and preferences, the technology recommends the correct size. By implementing this technology, Moosejaw reduced size sampling rates by 24 percent over a one-year period. It saw a 34 percent drop in size samplers and reduced sequential size sampling by 18 percent.

Ultimately, by better understanding shopper behavior, retailers can improve things like fit and personalized recommendations to ensure that customers love and keep the merchandise they buy.

Romney Evans is the co-founder and chief product and marketing officer at True Fit, a data-driven personalization platform for footwear and apparel retailers that decodes personal style, fit, and size for every consumer, every shoe, and every piece of clothing.