Consumers hate to pay for product returns, so when mainstream media looks at retail returns, that tends to be their focus.
A report by CNBC asks this question about retail return policies:
How is it that, living in a world where marketers can track our moves online and off like never before, when they can build sophisticated models factoring in what we buy, what we read, what we like, how we sleep, and our favorite song, online retailers are still charging for return shipping when they could be making so much more if they didn't?â€
It's an excellent question. But it doesnâ€™t go far enough.
A better question is: Why arenâ€™t retailers learning from the product returns they do get, so they can prevent the need for consumers to think about returns in the first place? Because when return rates go down, everybody wins.
Itâ€™s true that free returns policies often pay off: Research by Professor Amanda Bower at Washington and Lee University found customers who paid for their own return decreased their post-return spending at that retailer 75 percent to 100 percent in the next two years. Free returns drove customers to spend 158 percent to 457 percent more with that retailer on future purchases.
But free returns arenâ€™t the only way to foster repeat business. Retailers who take a closer look at what gets returned, and why, stand to gain back the ten times the investment in that analytics process. Imagine what free returns plus preventing returns can do.
Hidden gems in returns data.
Gaining insight into the cause of returns is critical, because, surprising to most retailers, 67 percent of all returned online purchases are the fault of the retailer and not the customer, according to research by Trueship. Hereâ€™s how that breaks down:
23 percent are due to the wrong item being shipped
22 percent results from a product looking and appearing different when it arrives than it did online
22 percent of ecommerce returns are due to a damaged item being received
By analyzing returns data as it comes in, retailers can quickly identify patterns in data that point to solutions: Perhaps a mis-slotted pallet is creating mis-shipments, a better photo can prevent discrepancies in appearance, and some items need more packaging materials in in the carton.
Returns data can also reveal insights into what customers like and donâ€™t like about products. If a retailer can learn things like: the sizes on those pants run small, this fabric is scratchy, those zippers get stuck and so on, they can make changes to prevent those problems: Adding guidance on sizes to the website, changing vendors, modifying the specs on a new order and so on.