Issues with sizing and color conventions has probably been an issue, well, since the invention of clothes. And with so much shopping suddenly driven online during the pandemic, combined with generally liberal return policies, apparel returns have exploded.
The widespread practice of “framing” in ecommerce clothing purchases – the ease with which a shopper can buy multiples of an item and return all but one – is another huge contributing factor. And this practice is exacerbated by inconsistent sizing, creating an unvirtuous circle.
The National Retail Federation puts the average apparel returns rate at 11.1%, a figure most observers say should be a good deal higher. NRF says ecommerce returns overall average between 20% and 30%.
According to data from Optoro, 50% of returns overall are returned to stock at the retailer, while 25% are returned to the vendor for credit, 15% are sold at wholesale to secondary consumers and 7% are sold to secondary consumers via DTC channels. The remaining 3% goes to a landfill, although increasingly returns not disposed through the other channels are ending up there.
While some sizing tech providers envision a world where sizing and color charts – and the standards they’re based on – disappear altogether and everything is solution and smartphone driven, that reality is likely quite a way off.
“At the end of the day, both the shopper and the brand want the same thing – an exceptionally positive experience, confidently selecting the correct size and reducing returns, which increases brand loyalty,” said Billy Pardo, Chief Product and Operations Officer for MySizeID, an Israeli startup with a sizing tech solution. “At the same time, fewer returns mean reduced operational costs, less waste and even higher revenue.”
Technology is coming to the rescue, in the form of various solutions aimed at cutting down on apparel returns based on bad sizing and color confusion, as well as data-driven standards applied across retail. They include tools that gather a range of data online as well as face-to-face with customers in physical stores to help cut down on apparel returns and create a better shopping experience.
The solutions addressing apparel returns are also ready-made for the COVID-19 era of contactless commerce, by either taking photos with a mobile app, having shoppers enter information on a retailer’s website or having a quick body scan in a mall studio.
Fit Analytics: Helping Brands Identify Target Groups
Sebastian Schulze, a co-founder and CEO of Fit Analytics, said ecommerce apparel returns have become a major cost driver and margin killer for companies, especially those suddenly shifting business online.
“When they grow online, they face different challenges,” said Schulze, whose customers include fast-fashion staples Asos, Uniqlo and Zara. “If you want to compete as a fashion brand today, you need to provide the best customer experience possible. A lot of shops just use a static sizing chart, and that causes a lot of frustration in an extremely competitive market. To stay ahead, you need to provide a better CX, and a lot of companies see technology as a tool to address the challenges.”
Fit Analytics’ core product is Fit Finder, an app that lets shoppers enter information on a retailer’s website and then makes product recommendations of the best-fitting items. It also has Fit Connect, which goes beyond sizing to gather data based on the customer journey to drive style recommendations. Fit Consultant and Fit Intelligence are retailer- and brand-facing tools that use machine learning to help them learn things like age and weight and height distribution among customers to optimize the product mix.
Schulze said Fit Analytics enables fast fashion brands to identify a new target group through demographic data, size and style preferences and produce a new collection in a matter of weeks. “They know based on the data on the data there’s demand for it,” he said. “They learn about the customers in the target group and act accordingly.”
Fit:Match: Physical and Digital Merging
One company bridging the physical/digital worlds is Fit:Match. At studios based in Brookfield malls in Chicago and Houston – with Dallas and Los Angeles coming online this fall – data is captured from a 10-second body scan that takes in over a dozen characteristics. Shoppers then enter various preferences and receive a number of recommended products, which can be purchased and picked up outside participating stores in the mall. They can get 3D measured in two minutes, and shop at stores including Under Armour, Express, Ted Baker, Nili Lotan, Paige and Good American.
The pilot in Houston in 2019 resulted in an 80% participation rate among shoppers who entered the Fit:Match studio, and 4,000 consumer sign-ups.
“The pandemic has greatly changed consumer shopping behavior, increasing the demand for contactless shopping experiences that provide enhanced safety,” said Fit:Match CEO Haniff Brown. “A recent First Insight study found that 65% of women feel unsafe trying on apparel in dressing rooms. Our solution eliminates the need for any shopper to have to enter a fitting room.”
Brown claimed Fit:Match only recommends products that have a 90% or greater likelihood of fitting a shopper, while hiding the rest. “It provides fit certainty that drives conversion and loyalty while boosting margins for our brand partners and lowering their costs associated with returns,” he said.
3DLOOK: Computer Vision and Neural Nets
Whitney Cathcart, a co-founder and CSO of 3DLOOK, said the confluence of COVID-driven store closings, safety concerns and booming ecommerce, combined with very liberal return policies, have made solutions like his suddenly popular to tackle apparel returns.
“Traditional stores were seeing return rates in the mid-single digits before ecommerce, but online can be upwards of 50% because everyone makes the process so easy,” Cathcart said. “Once you hand consumers something like that, you can’t take it back. And return policies have become far more lax during COVID, where what had been 30-day return periods are now in some cases up to 90 days. Think how that disrupts inventory, particularly if you’re selling seasonally.”
3DLOOK uses computer vision, neural networks and 3D statistical modeling to generate 3D models and 70 different measurements from two photos taken on a smartphone on any background on people wearing tight-fitted clothes. It is browser based, meaning there is no app download.
“The way clothing is designed is based on a fit model using average stats that a company can buy,” she said. “They know for instance that women on the coasts are generally thinner, and the middle of the country has extended sizing. It never made sense to me. If we can identify a brand’s actual customers, we can overlay data on patterns and grading rules. We’re not moving to a standard but to personalization. It maps product data against your body data and makes a fit recommendation based on you, not an aggregated average.”
GSI: Working to Move Apparel Standards Forward
GS1 US, which recently took over management of standardized apparel sizing and color codes from the NRF, said standards still have a major role to play. The codes have been used in retail systems for more than 30 years to help the industry consistently identify and classify products by size and color. They also provide a common language for product color and size identification for Electronic Data Interchange (EDI), which allows partners to exchange order and invoice information.
At GS1, the Product Images and Data Attributes Workgroup, with 63 members including retailers, brands and solution providers, is tasked with maintaining the color and size codes. It develops industry-specific guidance and best practices for sharing product attributes and images between trading partners to improve product data accuracy across channels. Members include Target, Macy’s, Nordstrom, Belk, JC Penney and PVH Corp.
Michelle Covey, vice president of partnerships for GS1 US, said the standardized codes help retailers, brands and manufacturers reduce errors due to manual data entry, cut down on new item setup times and enable automated sales analysis by size and color to help in assortment planning.
“When retailers and manufacturers work together, we can codify and share product information, making it more consistent for buyers, knowing they’ll always get this particular size, and inventory planners can know how many smalls or mediums are in stock,” Covey said. “The codes work on consistent product data that’s shared, so members can know what items are selling where, do sales analysis, what colors and sizes are selling well for forecasting. That’s where use of codes been very useful over time.”
Covey said solutions like virtual sizing tools can work alongside standards. “When you get into things like virtual mirrors, that’s where I see a blend of using codes for purchasing and sales, and using (tools) to do accurate fitting. How brands codify sizes are a bit different.”
For instance, she said, tech solutions need a standard way to identify color and size, relying on good product data to serve up information to shoppers.
“Although there is differentiation in the marketing language of size and color, the codes are foundational to inventory management,” she said. “For example, the color code for pink is 615, but the consumer would never know that. They would see it as ‘cotton candy’ online or as an alternative in a store or fitting room. Ultimately, color and size are two of the most important and basic product attributes and consumers expect them to be accurate and consistent.”
She said GS1 US will continue to work with industry stakeholders to ensure the codes evolve along with emerging technology like sizing solutions.
MySizeID: Aggregating and Analyzing Data
Pardo said MySizeID aggregates and analyzes data from brands’ sizing charts, inventory software, a proprietary anthropometric database and the user’s own measurements taken with their smartphone sensor, creating a unique profile. An on-page widget recommends the best fitting apparel for each individual customer. AI, big data and deep learning algorithms drive the process from analysis to recommendation to cut down on apparel returns.
“We create profiles of real body measurements so fashion manufacturers will be able to manufacture accordingly and more accurately tailored to the needs of actual people,” Pardo said. “The size won’t define the person anymore.”
She said MySizeID is working with GS1, while also advocating for global standardization of its solution with IEEE. Data from sources like GS1 is only one part of the “data cocktail” that the MySizeID algorithms analyze, Pardo said, including brand databases as well as variables like material composition and garment design, among others.
“GS1 will possibly bring some uniformity to the apparel and fashion industry and that uniformity in turn offers more size measurement clarity, which will increase the accuracy of MySizeID’s personalized recommendations,” she said.
She said MySizeID client Boyish Jeans, based in Los Angeles, reduced its apparel returns by 31%, while Turkish women’s intimate and activewear seller Penti saw them drop by 50%.
“As we continue to improve and optimize our algorithm, together with advancements in AI and ML technologies, the accuracy level will return,” Pardo said. “It’s not unreasonable to imagine that in a few years’ time personalized size matching for online apparel will replace size charts altogether and send return rates into the single digits.”