Solving the Return Season Problem with Retail Analytics

excess-inventory_730x280.jpgFor most of us, this time of year means a few different things: winter is inching closer to giving way to Spring, the Super Bowl is just a weekend or two around corner, and our New Year’s resolutions have been all but abandoned (come on, just admit it). But for retailers, the end of the year’s first month marks the end of something much more significant: Return Season. And that’s a really big deal. 

From the day after Christmas until the end of January, retailers can expect one in three gifts purchased over the holiday season to be returned. Returns during this single, month-or-so period account for approximately 24 percent of all returns throughout the calendar year, equating to more than $60 billion worth of goods. And as online shopping continues to grow, the amount of sent back merchandise can only be expected to increase.

But the problem isn’t just that retailers are not moving as many goods as they expected, but rather, the inefficiency in how returns are handled. Simply put, many retailers do not have the proper systems in place to manage the sudden surge of sent back items. An article published by Multichannel Merchant explains the issue well:

The traditional reverse supply chain is long and complicated, with goods traveling from consumer to retailer to vendor to liquidator to wholesaler to reseller and finally, to a secondary buyer. Many of these items lose their value along the way, and 30 % of them don’t even make it into the hands of another consumer, ending up in landfills.

The article continues to explain that the reverse supply chain problem is often worsened by large amounts of overstocked merchandise, saying the “changing buying habits are causing an increases in excess inventory,” and that “most overstocked goods return only a fraction of their original selling price.” Ultimately, Multichannel Merchant recommends several new technologies designed to ease losses originating from a complex reverse supply chain.

Yet, the problem is these solutions are simply treating symptoms, not solving the problem.

You see, each potential solution only promises to ease the burden of the overcomplicated return process. For instance, one purported answer “leverages carrier data to enable customers to compare options and determine the easiest, most affordable way to return their purchases,” while another recommends following the example of subscription-based rental companies like Rent the Runway, who claims to send back 60% of returned merchandise the same day they arrive at the warehouse. All of these solutions are valid in correcting a problem, but don’t address the root of why the reverse supply chain is over encumbered—overstocking. 

Briefly mentioned by Multichannel Merchant but expanded on by the Wall Street Journal is that changing customer habits are the reason unsold goods are piling up. Or in other words, consumers are more diligent in where and when they shop and retailers have failed to take notice, resulting in an over-stockpiling of goods that often times wind up in the garbage. 

With this being the case, shouldn’t the solution for retailers be to anticipate and meet these evolving customer preferences rather than make it easier to take a loss on returned merchandise? Shouldn’t retailers be aiming to reduce the amount of overstock in the first place through product assortment optimization? And shouldn’t these retailers be employing a retail analytics solution in order to determine the best way to meet their customers needs?

It’s true that retailers will never be able to completely eliminate the need for returns (everyone has that one impossible to shop for relative), and because of that a reverse logistic technology is a viable consideration. The point is, however, that the problems of overstocking would never be as immense as they are if retailers use the lessons—and the data!—of these past few holiday seasons to cut down on overstocked merchandise in the first place. Retail analytics can do that. 

It’s the solution we promise you won’t want to return.   


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Topics: assortment optimization, markdown optimization, retail analytics, returns

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