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How Retailers Can Keep Holiday Returns from Ruining the Season

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Holiday returns can add up to big losses. But using big data can help retailers gain a competitive advantage and help reduce shrinkage and fraud.

Holiday sales are the lifeblood of many retailers. But holiday returns can put a damper on end-of-year celebrations. Reportedly, 54% of holiday gifts were returned last year according to GiftNow, an e-gift giving platform.

With more people shopping online than ever before, more gifts arrive without try-ons or inspection. So... the shirt is the wrong size. The speaker isn’t compatible with the computer. Or he already has a socket wrench. That’s why rates for holiday returns are typically higher than the rest of the year, according to the National Retail Federation.

Surprisingly, men are more likely than women to exchange gifts, but one of every three consumers returns at least one holiday gift item. And all those returns add up to real losses for retailers — as much as $260 billion in merchandise last year alone.

Seamless and frictionless holiday returns

Returns are expensive, and they’re also the interaction that is most likely to cost you future business. If your customers think it’s too hard or too expensive to return something, they may start to look for someplace else to shop.

Returns can also be a positive experience that delights customers and keeps them coming back:

  • 92% of shoppers will buy again if returns are easy.
  • 79% want free return shipping.
  • 62% are more likely to shop online if they can return in store.

Leading merchants are adopting the “BORIS” (buy online, return in store) approach to appeal to consumers who no longer distinguish between shopping online and shopping in stores.

“Customers expect every transaction to meet their needs,” according to recent research by North Highland Consulting Company. That includes “a seamless and frictionless returns experience.” Integrated data from stores, e-commerce websites, apps and logistics can help you deliver that seamless return moment.

Fraudulent holiday returns

According to Reverse Logistics magazine, returns reduce the profitability of retailers by more than 4%. And the National Retail Federation’s 2015 Return Fraud Survey estimates that annual merchandise return fraud and abuse costs the U.S. retail industry between $9.1 and $15.9 billion annually.

Fraud, like the overall number of returns, is exponentially greater after the holidays. Three times as many products purchased online are returned, compared to brick-and-mortar store sales. And 32% of people don’t include a receipt (gift or original) when giving a gift, which increases exposure to fraudulent returns.

Returns of stolen or shoplifted merchandise, returns of used merchandise, or employees making fraudulent claims can drive retailers to adopt tighter return policies at the end of the year.

Big data and learning from your returns

Retailers who are prepared to handle holiday returns and reabsorb them back into the value chain will fair best during this seasonal crisis. How to do that? Big data.

Predictive analytics and big data from GPS-enabled smartphones, scanners and Internet of Things (IOT) enabled sensors can track inventory from store shelves to the customer’s front door — and back again. For retail, thoughtful return policies, big data and reverse logistics can create a competitive advantage and may help reduce shrinkage and fraud.

Analyze returns data to identify products that overpromised and under-delivered. It’s your customers telling you if they like your merchandising choices. High return rates from e-commerce channels may mean products aren’t displayed or described properly on your website. A holistic view of multi-channel sales and returns can help with inventory forecasting, pricing, and product replenishment.

Use all the logistics data you have from items coming back in to your warehouse, as well as items going out, to react more quickly, reduce errors, and be more profitable.

To learn more about big data for retail logistics get our e-book: Using Big Data to Optimize Distribution Strategy.

big data to optimize distribution strategy

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Topics: e-commerce holiday shopping e-commerce shopping fraud big data