As much as shoppers love to shop, they also love to return items, especially when it comes to online purchases. With omnichannel shopping continuing to grow, the volume of returns is expected to increase along with it.
As reported in Zebra’s recent Global Shopper Study, most consumers (82%) say they prefer retailers who offer easy returns, while most retailers (81%) say managing online returns is a significant challenge. Around seven in 10 of global and APAC retailers say the pressure is mounting to improve the efficiency and expense of managing online orders, returns, and the fulfilment process.
It is a conundrum. Returns are expensive, labour intensive, and may open the door to other issues like returns fraud.
With the increase of returns growing to US$1.8 trillion and impacting retailers globally, how can retailers improve their returns management processes while delivering the hassle-free returns experience that customers expect? With the help of artificial intelligence (AI), retailers can get an upper hand on returns year-round, from the holiday surge and beyond.
Engage returning customers seamlessly with AI solutions
E-commerce has revolutionised the returns game. Take the “bracketing” attitude adopted by many customers, especially when shopping online. They will purchase multiple colours or sizes of an item online, then determine which one to keep when items are received, and finally return the rest. As more shoppers choose to return online purchases in stores, it is becoming a bigger headache for retailers.
Wardrobing – also known as returning used merchandise – or returning stolen items or merchandise purchased fraudulently are other common returns issues retailers face. As much as retailers might consider tightening their return policies, it comes at the risk of unhappy customers. The bottom line is returners are going to return. And they are going to be loyal to the retailers who make this process easy for them.
Retailers have the opportunity to enhance the returns process, effectively addressing bracketing and other fraudulent return scenarios by leveraging AI. For instance, in situations where retail associates are unfamiliar with the returned merchandise or customers have questions about a retailer’s return or refund policies, retail associates can readily consult their AI-enabled mobile devices.
Through a contextual-based search, AI swiftly retrieves pertinent information to address queries, saving time and avoiding any potential processing hurdles. Consequently, retail associates gain rapid access to information, facilitating a smoother returns experience for customers.
Six in 10 retailers surveyed in Zebra’s Shopper Study say they are upgrading their returns management technology by 2026. In APAC, 74% of retailers surveyed are also in the process of upgrading, this is 12% higher than global retailers surveyed.
Proactively preventing high return rates: AI’s predictive insights
UsingAI predictive analytics can also help retailers reveal merchandise with high return rates. Similar to analysing a customer’s returns data, it can analyse at the item level to identify product patterns or trends.
The data could point to quality or sizing issues, for example, or identify if the online product description was misleading. In either case, retailers can take steps to correct the issue uncovered, whether it is resizing, improving quality and product descriptions, or even pulling the product, to prevent further returns.
AI demand planning enables retailers to predict merchandise return rates and adjust inventory levels for optimised inventory. For example, when a product is predicted to have a higher return rate, retailers can plan on keeping less of that product in stock. But a low return rate would likely mean maintaining a higher level of inventory. This insight gives retailers the ability to plan and get inventory levels just right, reducing returns and costs as well as improving overall efficiency.
Analyse, understand and predict customer returns
Returns are often attributed to practices such as bracketing and wardrobing. However, what about the underlying, less apparent reasons? Is it due to merchandise not meeting expectations, or are they habitual returners? Retailers can leverage AI analytics to uncover insights into these and other reasons behind customer returns.
By analysinga customer’s past purchases and returns, AI can identify patterns or trends which indicate or predict if a customer is more likely to return purchases. Once a retailer better understands why a customer is returning items, they can take steps to help prevent it from happening again.
Let’s say AI uncovers a pattern showing a customer who often purchases and returns clothing in a certain size, colour, or style. Leveraging AI’s power of personalisation, a retailer can recommend other sizes, brands, or styles for the customer to consider. According to research commissioned by Google Cloud, 75% of shoppers prefer brands that personalise interactions and outreach to them, so it is likely consumers will purchase recommended options, minimising the risk of return due to the size, colour or style AI has uncovered as an issue.
AI: Enhancing retail intuition with unmatched precision
While intuition is inherently part of decision-making in business, it is not humanly possible to predict with certainty what customers may return and how frequently, or which products might have flaws or simply don’t resonate with customers.
But it is possible to predict this better with AI. By harnessing its power, businesses can better anticipate customers’ return patterns, optimise inventory, and turn AI insights into action to minimise returns, while still providing the returns experience customers expect and deserve.
To learn more, please visit HERE.
Darren Bretherton is regional sales lead software solutions for ANZ at Zebra Technologies.