Will Innovation Fix Fashion’s Long-Standing Sizing Issues?
Many women are all too familiar with the frustration of inconsistent sizing on the high street.
A pair of jeans that fits as a size 10 in one store might be labelled a size 14 in another, leaving shoppers confused, disappointed, and unsure of what will actually fit.
This inconsistency has triggered a global surge in returns — costing fashion retailers an estimated £190bn a year — as customers struggle to guess which size corresponds to which brand.
It didn’t take long to find people dealing with the issue firsthand.
“I don’t trust high-street sizing,” one shopper tells me while browsing along a busy London shopping strip. “Honestly, I just choose whatever looks like it might fit rather than relying on the number on the label.”
She’s far from alone. Many women now order the same item in multiple sizes just to keep the one that fits, sending the rest back — a habit that has helped fuel today’s culture of mass returns.
A new generation of sizing tech
A growing cluster of tech companies are now attempting to fix the problem.
Tools such as 3DLook, True Fit and EasySize focus on helping customers choose the right size at checkout, using body scans via smartphone photos to suggest the most accurate fit.
Meanwhile, virtual fitting-room platforms including Google’s virtual try-on, Doji, Alta, Novus, DRESSX Agent and WEARFITS allow shoppers to create digital avatars and preview how items might look. These systems aim to increase confidence when buying online.
More recently, AI-powered shopping agents have begun entering the market too. Daydream, allows users to describe what they are looking for and then recommends options.
OneOff pulls together looks from celebrities to find similar items, while Phia scans tens of thousands of websites to compare prices and surface early “size insights.”
While these tools work at the e-commerce stage, a new UK start-up, Fit Collective, is taking a different approach: trying to prevent the problem earlier in the production process.
Founder Phoebe Gormley argues AI can potentially fix the sizing before clothes reach the stores.
The 31-year-old – who is no data scientist, rather a tailor – previously launched Savile Row’s first female tailors, making made-to-measure garments for a range of women.
“They would all come in and say, ‘high-street sizing is so bad’,” she tells me.
She says fashion’s current model is a “downward spiral” where brands make cheaper garments to offset huge return rates, which leads to unhappy customers and more waste.
Since launching last year, Fit Collective has raised £3 million in pre-seed funding, reportedly the largest amount ever secured by a solo female founder in the UK.
“As far as we know, we are the first solution comparing all the manufacturing data and the commercial data,” she says.
Phoebe’s new venture uses machine learning to analyse a range of data – including returns, sales figures and customer emails – to really understand why something didn’t fit.
It then turns this into clear advice for design and production teams, who can adjust patterns, sizing and materials before manufacturing begins.
Her system may tell a firm, for example, to take a few centimetres off the length of an item of clothing to reduce the number of returns overall. This saves money for the company and time for the consumer.
