As consumers demand more and more, one startup in the apparel space is committed to deliver less—90% less—to declutter the shopping experience.
Fit:Match, which this week announced its national rollout, uses a proprietary computer algorithm to select garments that fit an individual properly in all the right places while concealing from view the vast majority of items that won’t fit or flatter her body. At a time when Covid-19 concerns keep shoppers away from musty store fitting rooms, the service provides a way to try on clothes digitally, without touching anything, by browsing an apparel assortment online that’s curated to their precise body measurements.
“We will never show you something that you will find to be a poor fit. We will literally hide it from you,” Fit:Match CEO and founder Haniff Brown told me in an interview this week. “That sounds provocative but that is what customers told us they want. They said, ‘Show me things that fit me and hide the ones that don’t.’ ”
Though Fit:Match was founded prior to the coronavirus pandemic, like many in the fashion space, it’s needed to be mindful of consumers’ shifting mindset and health concerns. In a July 1 research note from Jefferies Equity Research, analysts wrote that Fit:Match “offers a contactless shopping experience that sits in the sweet spot of the post Covid-19 era.”
Fit:Match disclosed on Tuesday that it will launch the service in August with a popup studio at the Oakbrook Center shopping mall in suburban Chicago. The location’s open-air, high-traffic setting is designed to entice curious passersby—generally 18- to 35-year-old women—to stop in and inquire about the personalized, virtual try-on system.
Brown is hopeful for a repeat of Fit:Match’s successful pilot test at Houston’s Baybrook Mall, where strong shopper interest enabled the company to collect 300 million data points about consumers’ bodies and style preferences in three months’ time.
“We had a mind-blowing participation rate,” he told me. “Four out of five women, 80%, agreed to the process and agreed to give us their data,” he said, noting the nature of a popup drives urgency because shoppers know it’s temporary and may not be there the next time they visit the mall.
Creating urgency is an effective strategy for popup stores, said Mohamed Haouache, CEO of Storefront, a marketplace for short-term retail spaces. “It doesn’t surprise me that FitMatch saw this level of success. It shows just how effective a popup can be when done properly.
“Building a sense of excitement about your project and playing to people’s FOMO (fear of missing out) is one way of doing this,” Haouache added.
Following the Chicago August launch, Fit:Match’s next popup studios will open in September at the Glendale Galleria in the Los Angeles area and Stonebriar Centre in the Dallas area. All mall locations are owned by Brookfield Properties, a collaborative partner and strategic investor in Fit:Match.
Brown said he hopes to open locations in the nation’s top 10 media markets (DMAs) over the next 12 months.
The apparel brands consumers can shop through Fit:Match won’t be revealed until the Chicago opening next month. Brown said Fit:Match will launch with a household name anchor brand, three mid-tier apparel brands plus 25-30 smaller, direct-to-consumer clothing brands. To be included in its database, brands pay Fit:Match a commission on sales, flat monthly fee or annual contract.
“We want to differentiate how we are approaching this and from how we believe others have done this before,” Brown says. Indeed, digital fitting room technology has come and gone over the years, from Lands’ End
“Today’s customers not only want less clutter in their shopping experience,” Brown adds, “but they are used to it” thanks to the likes of Netflix
To deliver on this less-is-more, highly personalized proposition in apparel, Fit:Match assigns a fit score to every item of apparel in its database that is unique to each shopper and her body dimensions. Only those items achieving a fit “match rate” score in the top 10% for a given individual are displayed to that customer. The remaining 90% of items are hidden from view.
Fit scores are calculated using artificial intelligence, 3D body scanning and a computer algorithm driven by a matrix of four factors:
- Fit preference: Consumers who opt in to the service indicate their style preferences that may favor casual, loose-fitting clothes or tailored, body-hugging styles.
- Tech pack: Apparel brands participating in the Fit:Match platform furnish what’s known as a tech pack containing highly detailed product specifications covering precise bust, waist and hip measurements and other data about how a garment contours the body.
- Body measurements: Fit:Match’s 3D infra-red body scanning technology employs 18-20 cameras to capture a person’s body measurements, right down to width of a kneecap. The scan takes 10 seconds to capture 150 data points from the human body.
- Fabric composition: Lastly, the fit algorithm takes into account a garment’s fabric type, such as Spandex with lots of stretch to fit a range of body sizes versus more rigid leathers and linens, with far less elasticity.
“We believe brands in stores today show customers 20 times more product than they want to see. They put the burden on the customer to figure it out,” Brown says.
A data-driven solution that curates the assortment presented to shoppers not only eliminates what he calls “fit risk” for consumers, but can provide powerful data to brands about their customers. “In two minutes,” Brown said, the length of time it takes to enroll a customer in Fit:Match and scan her body, “we can capture information about a customer that will take an online brand 6-12 months.”