Why finding the perfect dress size is so difficult
Generations ago, women who wanted a new dress went to a seamstress . The seamstress would take careful, detailed measurements and create a dress that fit the customer’s body perfectly.
That might sound like a dream today, but there were serious limitations to the old approach : it was much more expensive, it took a long time, and customers were limited to the fabrics and talents available where they lived.
The fashion industry has standardized and today , most customers buy their clothes off the rack . Few people would want to go back to the old way of getting clothes. But one consequence of standardization is that women have limited choices in sizes . The specifics vary by country, but one truth is the same almost everywhere: every female customer must boil her whole body down to just a single number , the standardized size.
In the U.S., women’s dress sizes are available in even-number increments from 00 (petite) to 22, with the majority of women fitting between a size 2 and 16. But two women who both wear, say , size 8 may have much different proportions . So a size 8 might be the right choice for both, but the same garment would fit them very differently .
To make matters more confusing , a size 8 at one brand isn’t the same as a size 8 at another brand. Even within one brand, sizing may not be consistent across different product lines. Here’s why.
Fashion designers for retail stores typically work with a “fit model” for each line of clothing. The fit model is the size and demographic of the target customer of that product. Most luxury fashion brands pick a size 6 model of approximately the same age as the target customer. They design a size 6 for that model’s body.
The designs are very detailed; they include much more than just the standard measurements. Designers control the size of the arm holes , the tightness of shoulder seams , the height of the waistline , and dozens more smaller fit decisions. Once they have the perfect fit for their “fit model,” they adjust the measurements up and down for the other sizes.
Here’s what that means. If a brand designs a product for a size-6 fit model, then that’s it: there is no size-8 fit model; there is no size-10 fit model. To design the size 8, they just take the size 6 measurements and tweak them. To design the size 10, they tweak them even more. So the farther a garment’s size is from the fit model’s size—up or down—the more removed that garment is from a real human’s body.
So if you happen to have a similar body to the fit model, you’re in luck . But here’s a depressing stat . One industry study looked at 657 different women and found only nine had measurements similar to the fit model—that’s about 1.4 percent.
Okay, but let’s say you did the hard work and found the right size for you in each of your favorite stores. Is your job done? Not exactly. You see, a size 8 in one year isn’t the same as a size 8 in another year, even at the same store. Fashion analysts point to something called “vanity sizing.” A size 6 woman who gets a little bigger can still wear a size 6 as she gets older, because the size 6 is getting bigger.
So, “standardized sizing” might be a misnomer . There doesn’t seem to be much “standard” about it.
The confusion has real consequences . First, customers find it difficult to find an outfit that fits well. So millions—if not billions—of women are walking around in clothing that doesn’t fit their body they way they’d like it to.
And with the advent of online shopping, returns carry a real economic and environmental cost . About a fifth of online clothing purchases are returned , and 90 percent of returns are related to fit and sizing.
What can be done to create a better fit? There are a few unsatisfying proposals .
Some people think brands should follow strict size guidelines , so that an 8 at one store would be an 8 at every other store. But designers and industry analysts say this would make the problem worse.
At least today’s confused and varied sizing lets women find a brand and size combination that fits them well. More standardization in sizing would remove a lot of the options in the market: it would work great for the minority of people who fit the standard size perfectly, but not at all for the majority who are a bit different.
The other option is to move to sizing that takes into account multiple measurements. This makes sense, right? Why should women have to summarize their entire bodies in just a single number?
For women, the three most important measurements to get a good fit are bust , waist , and hip . But there are downsides to making outfits based on three measurements instead of just one measurement.
Brands would have to contend with a much larger variety of sizes. Now, they might make one piece in ten or twelve sizes. But if they moved to sizing based on multiple measurements, they’d have to make thirty, fifty, or more different size combinations.
It would be a nightmare to manage the inventory : they wouldn’t have a good idea of how much to make of each size with so many different size combinations. There would be fewer returns, yes, but there would probably be more total waste in the system, as many garments would go unsold . That would lead to higher costs . And consumers like lower prices.
There is a silver lining . New, more flexible fabrics give women—and men—more flexibility in finding the right fit. If an outfit would be tight in just one of your measurements, now the fabric can flex a little so you don’t have to go up an entire size .
Jeff’s take
In the old days of the shopping mall , you might only have five, six, seven stores to choose from. You were at their mercy and most of them had the same brand image. Tyranny . But now online brands can target their own markets, have fit models of different sizes, so that they make clothes that fit better.
Plus, I think data science , cameras, and better imaging should all help. A brand called Laws of Motion offers a dress in 99 sizes. So you can take a few photos, fill out a questionnaire , and get a precision size , based on multiple measurements. I like that.
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