Why Inaccurate Size Recommendation Tools Are Making the Fit Problem Worse

I rarely get this angry, but after observing the new size recommendation tools that are emerging on the market, I’ve reached my breaking point. Some of these tools are already backed by investors and being used by major brands. But I have a serious issue with the direction they’re taking.

Dear developers, AI researchers, and anyone without patternmaking experience, do you realize the disaster you’re causing by providing inaccurate size recommendation tools?

Do you honestly think the fit problem can be solved by someone who has never made a single pattern for clothing? I’m not here to throw shade at technology; I’m here to highlight a major flaw that could ultimately harm customers and fashion brands alike.

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Don’t install a size recommendation tool on your website if it skips these crucial factors – you could be losing both customers and sales!

Through years of experience – from creating thousands of made-to-measure garments to helping customers find the right size online – we learned what it takes to get sizing right.

When making made-to-measure clothing, there’s no room for mistakes – especially for one of the most important days in our customers’ lives.

We also did a lot of bulk production, but it was through made-to-measure items that we truly understood what makes a size the right one.

At SizeSense.ai, we don’t claim to be revolutionary. We’ve simply transformed our patternmaking and tailoring knowledge into code – knowledge that has existed for ages – and applied it to software.

So….Don’t install a size recommendation tool on your website if it skips these crucial factors – you could be losing both customers and sales!

❌ No 1:1 alignment between body and garment measurements

Most existing solutions rely on three flawed methods: analyzing past customer purchases, matching size guides across brands, or recommending sizes based on similar height and weight profiles. These approaches fail to account for each garment’s unique measurements, design, and manufacturing variations – often increasing return rates by misleading customers.

❌ Ignoring elasticity

A pear-shaped customer shopping for stretchy jeans may be told they won’t fit if fabric stretch isn’t considered – costing fashion brands a sale!

❌ Overlooking clothing design

A customer may wear one size in a loose, elastic dress but need a size or two up for a non-stretch shift dress – increasing the chance of returns.

❌ Ignoring body shape

Two people with the same height and weight can have vastly different proportions, affecting how a garment fits. Ignoring this leads to incorrect recommendations and higher return rates.

❌ Neglecting fit preference

Fit preferences vary – 17% of women prefer fitted clothing, 20% prefer a looser fit, and the rest opt for a regular fit. Ignoring this further increases return rates.

By addressing all these elements, SizeSense.ai ensures customers can confidently select the right size – reducing returns, increasing sales, and improving customer satisfaction.

• We align customer body measurements with the measurements of the specific product they’re interested in.
• We consider both measurements and body types, thus accommodating diverse body shapes.
• We provide tailored recommendations considering fabric elasticity, clothing design and customer’s wear preferences.

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For every tool I tested, it was the same result. No matter the design, elasticity, or whether the products came from different brands, I was always recommended the same size. Whether it was an elastic dress, a fitted blazer, or a loose-fit jacket – it didn’t matter.

This is exactly the kind of error that fuels the growing frustration with online shopping. Customers receive their orders, try them on, and send them back because the fit is wrong.

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I understand the urgency in creating a size recommendation solution to one of the fashion industry’s biggest problems: fit. The problem isn’t just a customer inconvenience; it’s a real issue that’s affecting the bottom line of fashion brands.

But if you think AI or machine learning alone can solve this, think again. Technology is powerful, but it can’t replace the craftsmanship of a seasoned patternmaker. Craftsmanship should be embedded in your code. You can’t just throw a bunch of data into an algorithm and expect it to know how to design for the intricacies of human body shapes and fabric behavior.

If you don’t understand how to incorporate that craftsmanship into your solution, please don’t release your code. It’s like me starting to use Copilot and calling myself a developer who can write real code. The end result is more harm than good.

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The road to solving the fit problem is long and requires more than just a few simple metrics and data points. It requires a deeper understanding of clothing design, fabric properties, body types, and sizing consistency across different brands.

AI can help, but it needs to be grounded in the real-world expertise that only comes from patternmaking and fashion design. Until that happens, these tools are not the solution we need – they’re just another band-aid on an ever-growing wound.

So, to the developers and AI experts out there: before you go live with another “AI-powered” tool, consider this: if you don’t know how to incorporate the necessary craftsmanship into your code, maybe it’s time to partner with someone who does. The future of fashion fit depends on it.