The post-holiday returns season has descended on retailers. The National Retail Federation reported retailers processed $743 billion in returns last year, nearly 15% of total retail sales, with the volume frontloaded in the first quarter of the year.
The bigger the retailer, the greater the impact. Plus the return rate is even higher for online purchases, 18% of sales versus 10% for in-store purchases. That makes returns an even greater headache for AmazonAMZN, the nation’s second largest retailer than it is for number one WalmartWMT, which only generates about 15% of sales online.
Further complicating the returns picture is that clothing items are much more likely to be returned than other products. The NRF doesn’t put numbers to it, but Coresight Research estimates nearly one-fourth of online apparel purchases are returned.
Problems with size and fit account for over half of all fashion returns with the common practice of bracketing orders by buying an item in multiple sizes adding to it.
The high rate of fashion returns does a number on fashion retailers’ bottom line. The combined costs of shipping, processing and restocking can amount to about 66% of the price of the product, according to reverse logistics firm Optoro.
Ounce Of Prevention
Because an ounce of prevention is worth a pound of cure, Amazon has introduced an AI-powered assistant to help fashion customers find their best fit, which can be a special problem for a multi-brand retailer like Amazon. A medium in a particular brand might be a small for one and a large for another.
The new fit finder application used a learning-base algorithm to compare the sizing differences between brands, incorporate data from customers’ product reviews and the individual customer’s past fit preferences to recommend the best-fitting size for the customer.
Amazon explains:
“The algorithm anonymously clusters together customers with similar size and fit preferences, and products with a similar fit. From there, the algorithm learns from millions of product details, such as style, size chart, and customer reviews, and billions of anonymized customer purchases. It also takes into account sizes bought and kept by similar customers for the same product, or for similar-fitting products.”
Making It Easy For Customers
Customers frequently turn to customer reviews for guidance on sizing. The AI-system processes all customer reviews using large language models (LLMs) to gather details about size accuracy, fit, and fabric stretch to recommend whether a customer should size up or down on a particular item. The review highlights are displayed in an easy-to-read summary, freeing customers from manually parsing individual reviews.
Amazon has also improved the sizing charts to standardize them across different brands. Using LLMs, it extracted size chart data from its many different vendors, processing the various size inputs to present a simple-to-understand size chart that is standard across the platform.
Helping Vendors Too
Amazon is leveraging all this machine learning in a Fit Insights Tool to help its vendors better understand customer fit issues to correct inconsistencies at the source.
“By leveraging this data, brands can better understand customer fit issues, improve how they communicate sizing to customers, and even incorporate the feedback into future designs and manufacturing,” the company stated, and added, “This helps brands reduce fit-related returns and more accurately list their items for customers.”
Customer Obsession To The Fore
Returns aren’t just a headache for retailers; they are a headache for customers too. Nobody likes the hassle of receiving an anticipated purchase, trying it on only to find it doesn’t fit, then to turn around to package it back up and ship it off. Even if they aren’t charged for the return, which some retailers are starting to do, returns still take time, which is a cost to customers.
The prospect of returns takes the joy out of online shopping and when it comes to fashion, joy is what it is all about.
By addressing the number one reason for fashion returns – improper fit and unpredictable sizing – Amazon is fulfilling its customer-obsession mission and reducing the cost of returns for itself and its vendors.
“We’re not competitor obsessed, we’re customer obsessed. We start with what the customer needs and we work backwards,” as Jeff Bezos famously said.
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Eugen Boglaru is an AI aficionado covering the fascinating and rapidly advancing field of Artificial Intelligence. From machine learning breakthroughs to ethical considerations, Eugen provides readers with a deep dive into the world of AI, demystifying complex concepts and exploring the transformative impact of intelligent technologies.
