One of fashion’s biggest problems is also its most fundamental: fit. With no standardized sizing across the fashion industry, ill-fitting clothes and subsequent returns are damaging consumer trust, brands’ balance sheets and the planet. Vogue Business’s recent consumer sizing survey showed just how big the problem is getting, at the same time that brands are grappling with a broader luxury slowdown. Findings show that poor fit (43%) and inconsistent sizing (36%) are among consumers’ top deterrents to purchasing garments from a brand or retailer, and ill fit is the leading cause of returns, at 38%.
Few people have spent as much time thinking about fashion’s fit problem as Phoebe Gormley, who founded the first women’s tailor on London’s Savile Row when she was 20 years old. “I spent 10 years listening to women complain about sizing. No matter the age, no matter the price point, no matter where they shopped, every woman said the same thing to me: why is sizing so shit?” she tells Vogue Business. “It really feels like the fashion industry has lost all sense of garment sizing.”
Gormley thinks AI might be the fix. She founded a fit operating system for fashion, Fit Collective, which uses AI to provide brands with the data they need to improve how clothes are made, using size and fabric insights from customer returns. Today, the startup is announcing it has closed a £3 million pre-seed funding round from AlbionVC, Superseed, True and January Ventures, with a £324,000 UK government grant — funding she says will mainly go toward hiring engineers to work on the machine learning behind Fit Collective. Since launching in late 2023, the startup has brought on 10 clients, including Rixo, Boden, Ro Zo, L’Estrange, and The Sports Edit (part of Marks Spencer). This has given her a wealth of data to analyze where fashion’s sizing falls short.
Why fashion is getting fit wrong
Sizing is a problem that Gormley says plagues women more than men. While men’s return rates hover around 15%, womenswear, depending on the price point, jumps to 40–50%, per Fit Collective findings. For luxury womenswear, Gormley says, return rates tends to be even higher at around 60%. These gender discrepancies are largely down to womenswear designs deviating from the original “blocks” (i.e., size templates) more than menswear, thanks to more elaborate designs and a larger range of fabrics, from super lightweight to hyperstretch, skewing fit. As brands start to size their garments based on previous garments, this leads to more deviation from the original sizing blocks over time.
“For luxury, the returns problem is even higher, because customers purchase less and consider the garments more — meaning it’s a more careful decision, but also that luxury brands are sitting on a lot less returns data than mass clothing brands are,” Gormley says.
Gormley admits fundraising for the startup’s pre-seed was challenging and fit tech is investors’ “pet hate”, owing to the dozens of fit tools already on the market that have struggled to make a dent in retailers’ and brands’ return rates. Existing startups typically focus on developing AI-powered body scans that require the customer to take a photo of their body and then estimate their measurements, or a “find my size” button that’s integrated into retailers’ product pages that asks for the customer’s height, weight and the size they typically wear. Both introduce friction for the customer and require quite a bit of data input, meaning they’ve failed to live up to their promise. Across Fit Collective’s existing clients, Gormley says only 3% of consumers use these “Find my size” tools.
Beyond this customer input requirement, the biggest challenge brands are facing is the variation between their own sizes from one item to the next. At one high street brand, Gormley analyzed the retailer’s published garment measurements and found a 66cm difference between the smallest and largest shirts labelled as size 12 across 179 women’s shirts it stocks.
“When I was working with my tailoring business, I could see everyone going down the website update “find my size” route, but no one ever thought: why don t we just make the product fit more consistently in the first place?” she says.
Gormley hopes the AI tools her startup is developing can help brands standardize their sizing. Clothing brands — especially mass retailers — are sitting on a huge amount of data from customers’ transactions and returns reasons forms. Transaction data is fed back to production teams, who may plan to introduce more stock in the coming year for a jumper that’s sold particularly well this fall. But reasons for returns aren’t always treated in the same way.
How it works
Gormley has designed Fit Collective’s software so that brands and retailers can begin to use the data that’s currently being overlooked. AI has opened up the ability for the startup’s software to analyze returns, fabric behaviour and sizing inconsistencies and collate them all in one place — what she calls a “co-pilot to brands during production” that helps them improve products through smarter sizing decisions and subsequently reduce returns.
Eighty per cent of Fit Collective’s product is a behind-the-scenes software product that brands can use to analyze the performance of their garments. Its dashboard pulls insights on each SKU (stock keeping unit) and provides a red, amber or green rating for its commercial success vs. its fit success. It works this out from the sell-through rate, return rate, lost value of returns and the proportion of the overall return rate. It gathers insights on the product’s fit and fabric quality from all the return information provided by customers, and recommends actions on where to report the data, based on other sources, like the brand’s manufacturer logs. Finally, it predicts the return rate percentage and predicted revenue changes for the brand if it makes the recommended changes.
For brands and retailers whose websites are built on Shopify, the platform is designed as a one-click install Shopify app that links up with the brand’s API keys to their other data tools. Linking up to a brand’s transactions and returns is essential — then other data points, like reviews and product lifecycle management (PLMs), can be added to improve insights. If a brand isn’t on Shopify, Fit Collective can be set up via an API connection with their data warehouse.
A software-as-a-service startup, Fit Collective operates on a subscription business model. Brands’ subscription costs are based on their revenues and return rates — Gormley says a womenswear brand with $10 million revenue can expect to pay £1,000 a month. It’s an investment that’s designed to pay off with time. Depending on the brand, it can take six to 12 months.
The startup also offers consumer-facing insights on the website that feeds the fit data into a product page so that consumers are able to read about how a garment fits before purchasing — this is much like the “find my size” tools already on the market, but Gormley says that by weaving this into the product description (PDP) itself, Fit Collective captures 100% of consumers, rather than the smaller percentage that bother going through the “find my size” data input steps. Gormley says that so far, these website updates have made the startup’s brand clients their “whole annual contract value back in three months”.
“The 80% production piece is obviously a slower beast. Depending on the brand, it can be six or 12 months before they stock new items, but within that time, they have our consumer-facing sizing recommendations live on the PDP that begin to make those returns come down,” she says.
As OpenAI’s ChatGPT and Google make further inroads into shopping, Gormley says the platform’s integration with Shopify — the ChatGPT partner for integrated checkout within the chatbot platform — will enable the startup’s sizing recommendations to be pulled through by these AI platforms and offer consumers more accurate fit advice away from brand sites, too.
Beyond this, Fit Collective doesn’t necessarily plan to move into the opposite end of the fit tech funnel, virtual try-on. Instead, she predicts that Big Tech companies like Apple could extend their in-camera measurement apps to help work out body measurements — most likely in a health data wrapper — so that each consumer can log their measurements a couple of times a year.
“I’m excited about a future where consumers know their measurements in their phone and are able to go to ChatGPT shopping or Google shopping and search for a white pair of jeans,” Gormley says. “Then, thanks to our technology being plugged into brands’ sites, the LLM [large-language model] will be able to say from the 2,000 results, we think these 100 pairs of jeans are actually going to fit you.”
But in the meantime, Gormley says the “massive sustainable financial opportunity” for the fashion industry is “helping brands make the right stuff”.
“If we can help retailers make better products, the return rate is going to come down, which then means that they can actually invest in making products better,” she says.
Gormley describes a “negative spiral” that brands are currently trapped in, where they make products quickly and cheaply and they’re returned, which then has a big financial impact. This financial hit leaves brands with less money to play with year on year, which results in clothes being made more cheaply each year, too.
“I want our software to halt that spiral and make it an upward curve, where brands have the confidence to invest in making their product better a year in advance,” Gormley says.
“They’ll do this because they have peace of mind from actual data that the garment will then fit customers better, and finally, it isn’t going to return at 60%.”
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