SEE IT. LIKE IT. BUY IT. REPEAT. FAST FASHION GETS FASTER
Jessie Zeng was scrolling through her Instagram feed in 2016 when a post caught her eye. It was a photo of Gigi Hadid sporting a pair of pearl-studded jeans at a Paris Fashion Week event. Below the image were more than 50,000 comments, nearly all of them asking: Where can I buy those jeans?
A self- described fashion obsessive who has been sharing her own looks with her 30,000-plus Instagram followers since her undergrad days at MIT, Zeng scoured the web for the answer, only to find it was nowhere: The jeans had been custom-made for Hadid.
While trends used to be set twice a year in the pages of Vogue and on Paris runways, now they sprout up daily from the Instagram feeds of people like Hadid, the American model with 43.4 million followers. Retailers know fashion’s center of gravity has shifted, but they haven’t been able to capitalize on it.
But Zeng, 26, believed social media—Instagram, in particular— was powerful enough to alter that status quo. “For the first time ever, there is an entire feedback loop existing on a platform where people tell you exactly what they want to buy, and you can create it for them,” she says. The consumer dollars are clearly there. Fashion and apparel e-commerce sales in the U.S. are heading to $171 billion in 2022, says eMarketer, up from $104 billion in 2018.
Choosy, the data-driven shopping platform Zeng launched earlier this year, is her attempt to harness that loop. “It takes us about three days from seeing it on a celebrity on Instagram to having a sample made up,” says Zeng. “We do anywhere from 30 to 60 designs every month.” And because everything is made to order, items are stitched and shipped to consumers within two weeks of purchase, mitigating the inventory problem plaguing larger fast-fashion brands. It’s a hybrid of Stitch Fix, the personal styling delivery service, and bespoke clothing, says Paula Rosenblum, managing partner and retail technology analyst at RSR Research.
In figuring out how to speed up the clothing supply chain, Zeng had the benefit of family expertise. Her uncle owns textile factories in China, where she was raised. After an eight-month stint trading currencies fresh out of college, she moved near Beijing and spent two years managing a few of her family’s factories, giving her an up-close look at operations. “Without those years of experience, it would have been completely impossible to do this,” she says. Choosy works with a network of about 200 small, agile textile factories, making it possible to produce a diverse range of items in small quantities.
In figuring out what to make, she had the benefit of her friend Sharon Qian, who was working on her PhD in applied math at Harvard. Zeng persuaded Qian to join her startup, and Qian wrote algorithms to analyze the comments under Instagram photos and rank the popularity of specific items using natural language processing. This enables Choosy to scan millions of comments for what Zeng calls “buying intent.” By coupling that intent with existing sales data, the machine learns the attributes of items that sell well—in terms of celebrity connections, silhouettes, colors, and styles—and assigns a relevancy score to rank future photos.
Today, Zeng works with 35 employees in a downtown New York City office, as well as 15 people in China who focus on supply chain management. “Choosy really represents social commerce 2.0,” says Charlotte Ross, an associate at New Enterprise Associates, which raised Choosy’s seed round of $5.4 million. “Customers are the ones deciding what gets made. It’s the way people will ultimately want to shop.”