Holiday returns can add up to big losses. But big data can give retailers a competitive advantage and 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, 17.8% of holiday gifts were returned last year according to the National Retail Foundation.
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 two 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:
- 97% of shoppers will buy again if returns are easy.
- 79% want free return shipping.
- 48% say buy online, return in store is an important option.
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.
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 into 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.