Why sending a man to the moon is easier than finding jeans that fit
The Atlantic , December 2007
As a teenager, I squeezed into size-12 jeans. Over the past three decades, I’ve put on about 20 pounds, mostly below the waist. I now wear a size 6. People in the garment business call that bit of flattery “vanity sizing.” Sizes aren’t what they used to be.
But some things haven’t changed. No matter how low the digit on the hang tag, trying on clothes is still a frustrating, even traumatic, experience. Though designed as a mere convenience, clothing sizes establish an unintended norm, an ideal from which deviations seem like flaws. There’s nothing like a trip to the dressing room to convince a woman—fat, thin, or in between—that she’s a freak. Her torso is too long for the jacket or too short for the dress. Her arms are too short for the blouse that fits her bust. Her seat is too flat for the pants that hug her waist. Her hips nearly split the skirt that fits her waist. The more tailored the garment, the greater the problem. (Men’s clothes are easier, because they tend to be looser and because, as one industry expert puts it, men “have fewer bumps.”) Jeans are particularly troublesome. With its body-hugging fit, America’s egalitarian uniform provides little room to hide deviations from the norm.
And nobody’s normal. Sizes are standardized. Bodies aren’t.
Clothing sizes reflect a classic modern dilemma, a conflict between human heterogeneity and mass production. Standardized sizes made inexpensive, off-the-rack garments economically feasible. They gave shoppers a reliable guide to finding clothes in self-service shops. (Historically, the biggest advocates for standard sizes were mail-order catalogs, whose customers couldn’t try on the clothes they were buying.) Standardized sizes seemed efficient and scientific. Clothes could be as predictable as screws or frozen peas—and as regimented and impersonal as an assembly line.
From the 1940s through the 1970s, the U.S. government established and maintained size guidelines, using data from about 16,500 women, including 6,500 members of the Women’s Army Corps measured during World War II. The guidelines specified the proportions that defined, say, a Misses 12 or a Junior 7. These standards were always voluntary, however, and over time they broke down. As Americans got fatter, garment makers discovered the marketing wonders of vanity sizing. Equally important, makers of different brands found that different proportions—a larger waist-hip ratio or narrower shoulders—could attract loyal customers who fit that profile. Every deviation from the standard represented a potential market niche. Although individual brands still adhere to in-house size standards, competition killed national standards. The government got out of the size-specifying business in 1983, and nowadays only sewing patterns adhere to the old sizes.
Those sizes weren’t all that efficient at fitting the population anyway. In 2002 and 2003, an industry consortium called [TC]2, originally the Textile Clothing Technology Corporation, used computerized 3-D scanners to collect precise measurements from about 6,300 women and 3,600 men. Unlike the young, almost entirely Caucasian women in the government’s earlier research, these subjects represented a broad cross section of the population. The most striking result: Not many American women have the hourglass figures assumed by most apparel sizes. This is a question of shape, not weight; a recent study in South Korea, where women are much smaller, found that hourglass proportions are even rarer there.
More significantly, [TC]2 scanner data revealed just how varied body shapes are. The scanners use white-light beams to create a body-shaped “point cloud” of 40,000 data points that captures not only about 200 different body measurements, including six different ways of measuring the waist, but also posture and left-right asymmetries—much richer data than a tape measure can collect. “When you scan someone, you get an image on the screen made of X-, Y-, and Z-coordinate data that is assembled on the screen. You have that person,” says Susan Ashdown, a professor in the department of fiber science and apparel design at Cornell University and a leading researcher in the small community of scholars trying to improve clothing fit. You can’t recognize yourself from a list of measurements; you can from a point cloud, because everyone’s body is different.
BODY SCANS of five distinct size 8 body types
“There’s an incredible amount of variation, much more than we’re aware of,” says Ashdown. “When we see a clothed population, we don’t really see the range of shapes that are underneath that clothing. Thank goodness.” Clothing creates the illusion that bodies fit an aesthetically pleasing norm. And that illusion depends on getting the fit right. Garments that bunch, pull, or sag call attention to figure flaws and often make people look worse than they would without clothes.
In a tough apparel market, “better fit” has become the latest competitive weapon. Some garment makers are simply tweaking the proportions of their standard sizes to include more people, by, for example, enlarging waists or not automatically making legs and sleeves longer as torsos get wider. (The easiest way to fit more customers with the same number of garments is to make waists a bit larger, reducing the difference between waist and hip measurements.) Others have explicitly expanded their fit offerings, selling several different proportions in each size. Lane Bryant recently introduced three categories for each size of jeans: yellow, red, and blue, corresponding to different waist-hip ratios, with descriptions posted prominently in store windows. Many brands mingle sizing and style variations. In the Joe’s Jeans lineup of Chelsea, Cigarette, Honey, Lover, Muse, Provocateur, Rocker, Socialite, Starlet, and Twiggy styles, each features not only specific proportions but also a specific leg shape and rise.
By reducing standardization, all these approaches raise manufacturing and inventory-management costs. And, as the dizzying list of Joe’s styles suggests, more variety can confuse shoppers. Instead of the old conflict between mass production and individuality, we now face a new, postmodern problem: a conflict between choice, which accommodates human variation, and information overload. Proportions vary enough by brand and style to promise a good, or at least a better, fit for just about everyone, if you look hard enough. But looking takes time, effort, and mental energy.
In his 2004 book, The Paradox of Choice, the Swarthmore psychologist Barry Schwartz uses shopping for jeans as his kickoff example of too much choice. When his old jeans wore out, Schwartz went to the Gap to pick up a new pair, only to confront five fit choices in every size: slim, easy, relaxed, baggy, and extra baggy. He agonized over which was right for him:
Finally, I chose the easy fit, because a “relaxed fit” implied that I was getting soft in the middle and needed to cover it up. The jeans I chose turned out just fine, but it occurred to me that buying a pair of pants should not be a daylong project … Before these options were available, a buyer like myself had to settle for an imperfect fit, but at least purchasing jeans was a five-minute affair. Now it was a complex decision in which I was forced to invest time, energy, and no small amount of self-doubt, anxiety, and dread.
Not everyone, of course, will recognize the good old days when purchasing jeans was a five-minute affair. (I certainly don’t.) For many customers, having explicit labels reduces trial and error. Instead of going from store to store and brand to brand trying on garments in search of one with proportions close to your own, you can narrow the search to those that at least claim to resemble your shape. And, unlike Schwartz, not everyone is satisfied with clothes that bag or pinch. Surely we can do better than forcing everyone back into the old ill-fitting molds. Schwartz’s social criticism suggests an entrepreneurial opportunity.
Why not scan shoppers and let them order clothes that fit perfectly? A London company called Bodymetrics offers just such a service, with scanning “pods” in Selfridges. But Bodymetrics jeans are pricey, £450, or about $900, a pair. No matter how high-tech the measurement process, custom clothes are extremely labor-intensive. There’s a reason mass customization hasn’t swept the jeans business. Pattern pieces aren’t as modular as Dell computer components. Changing a hem is relatively easy. Changing proportions, like the distance between the knee and the hip or the exact curve of the butt, is much trickier. Each piece has to be revised, and then the pieces have to go together smoothly. Designing and cutting a new pattern for every customer eliminates many of the economies of industrial production.
A more promising approach is to use scanner data to match a customer’s shape with a store’s inventory, a service Bodymetrics also offers. Fit experts envision a future in which you’d carry your body scan in your cell phone or on a thumb drive, using the data to order clothes online or find them in stores. But who’s going to pay for all those scanners, which cost about $35,000 each, and the staff to run them? Why would Bodymetrics or Macy’s give you information you might use to buy jeans at Nordstrom or the Gap? And who’s going to maintain the databases of garment measurements? Mall operators might conceivably install scanners to attract customers to their shopping centers, spreading the cost and benefits over all their tenants. But none has done so. Scanner technology is appealing—it’s really cool to see a 3-D model of yourself—but so far the business problems have limited scanners mostly to research and a small luxury market.
“Scanning was interesting 10 years ago,” scoffs Rob Holloway, who bought one of the first [TC]2 scanners when he was a Levi’s executive. “Now it’s like a dinosaur which is still lumbering around.” Holloway’s latest venture takes a different approach. Founded in 2005 and located in a converted bakery across from the Pixar campus in Emeryville, California, Zafu has hand-measured thousands of women as well as 10,000 individual garments, including 500 different styles of jeans and 150 styles of bras (another hard-to-fit garment). The company had the women try on garments and talk about what they liked in the fit. Combining those comments with information on the fit of the particular styles the women tried on, the company developed a simple list of online questions—such as whether your jeans tend to gap at the waist—that segment customers by shape, allowing the Zafu Web site to suggest specific popular styles and brands likely to fit. For my extreme hourglass figure, Zafu recommended styles that included Joe’s Jeans’ Honey, Baby Phat’s Signature Jean, DKNY’s Stretch So-Low-Lita, and Christopher Blue’s Lloyd’s Sister.
Unlike Lane Bryant and other clothing companies, Zafu doesn’t require customers to figure out for themselves which category they belong in. Its system can segment body shapes into many more categories than a shopper could remember or a single brand or retailer could fill—about 50 rather than the usual three, five, or seven. The more narrowly defined the categories, the more likely the system is to find styles that fit. The perfect system, after all, would give every customer her own unique category, tailored to her individual shape. “You have to be more granular in the way that you think about shape,” says Holloway. By narrowing thousands of jeans styles down to a manageable handful, Zafu gives shoppers a viable alternative to Schwartz’s backward-looking prescription. It preserves the advantages of choice but eliminates the information overload.
What Zafu doesn’t do is tell you which size to buy. Its system doesn’t ask for body measurements, which amateurs are notoriously bad at taking. Besides, some elements of fit are too subjective for a computer. Am I a 6 or an 8? That depends on how tight I like my clothes. It also depends on how obsequious I expect the vanity sizing to be. The only way to find the right size is to try on the clothes. But at least you know where to start.