Returns is All in the Data … But Do We Have Enough?

It’s amazing how seemingly crazy ideas come to you when you least expect it. This happened to me recently while reacting to a tweet from retail analyst Andrew Busby. Andrew had commented on the news that ASOS, one of the largest U.K. online fashion retailers, had changed its returns policy. ASOS was effectively reserving the right to look at the returns history of consumers with the idea that for serial returners they might take some form of action.

ASOS’ strategy made absolute sense to me — returns is a massive issue for retailers, particularly in the fashion world. If your return rate is 30 percent, then that's 30 percent of your inventory you have on your books that you cannot sell until it has been processed and put back into inventory. The cost of this is huge, albeit it may not be fully understood just how huge. According to Gartner, it's estimated that retailers are forfeiting 10 percent of sales revenues to product returns.

We've seen retailers begin to test programs aimed at minimizing product returns. For example, Amazon Wardrobe is offering larger discounts and credits when you don’t return items. Furthermore, there was Boden's rather interesting returns policy that read “If you are a pathological chancer and simply can't help yourself, we recommend trying it on with our competitors instead.”

But what always hit me with these actions is what is there to stop a serial returner from shopping elsewhere? And this is where my idea came from: What if you could collate data from shopping history across multiple retailers?

Retailers are unlikely to want to share this data with each other for all sorts of competitive reasons, but what about an intermediary — similar to how a credit rating agency such as Experian or Equifax does when assessing you or I for credit. These companies don’t have any data of their own; they collect it from creditors and other organizations in order to generate a "credit score" that's then sold back to creditors to determine if you or I are worthy of being loaned funds.

GDPR and other regulations notwithstanding, I really cannot believe some company hasn't tried this. Publish APIs to allow easy collection of purchase history data from retailers, and then use predictive analytics to generate a "returner rating" predictive model. This model would be derived from all of a consumers’ purchases and evaluates the rate at which they return products. The model would thus predict how likely a customer is to return an item when they purchase.

This predictive model could be sold to retailers in a similar way to a credit score is sold to banks. Retailers would then use it as they deem appropriate. For example, customers deemed as low return risks might get free returns, while customers deemed high return risks have to pay postage for the return, effectively creating a disincentive to buy then return.

Applying this in real time during the purchase process — leveraging real-time streaming analytics to evaluate the predictive model along with other information about the customer and what they've done while browsing — could allow specific incentives to be applied at that point. For example, a discount that might be later applied if items are not returned.

Purchase history from one retailer alone is simply not rich enough to produce a model like this. This is yet another example of how retailers need to work together within an intricate ecosystem to flourish — even if it's with the help of a third party. It’s all in the data, but the data needs to be opened up and silos removed.

Oliver Guy is the global industry director, retail at Software AG, a company specializing in retail digital transformation and omnichannel technology strategy.