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How Taking Consumer Body Data Upstream Can Close Fashion’s Fit Gaps

As technology solutions crop up to help consumers find the garment or shoe that will fit or flatter them the best, the greater availability of body information has unlocked opportunities to turn product development and retail allocation from a guessing game into more of a science.

Historically, fashion companies have arrived at their size charts without the benefit of knowledge about the target end consumer. For instance, apparel and footwear has typically been designed in one sample size and then scaled up and down using longstanding tables. This means that there are often customers who are underserved as charts do not always match up to real bodies or feet.

Several companies slated to exhibit at the ShopTalk tech conference this September are offering alternatives to this often out-of-date practice.

“There are a lot of things fashion producers can do when they know the body shape data of their shoppers,” said Jason Wolf, head of sales North America at Fit Analytics. “Brands can design clothes for their actual shoppers, not just theoretical physical personas. With high quality data, we are able to bridge the gap between what a brand thinks their customers look like and what they actually look like.”

Fit Analytics’ size advisor asks consumers to input information such as their height, weight and how they prefer clothing to fit, and then serves up a size suggestion. This might be based on other shoppers’ own size choices, return records or a comparison to products that the customer already owns in other brands. The data input by consumers is then fed directly back to the partner retailers, allowing them to use it to fuel everything from targeted marketing campaigns to dress form specifications.

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Leveraging the size of its database, culled from billions of fit suggestions delivered over the past decade, Fit Analytics also has solutions that center on helping companies plan their product development and make more informed business decisions.

Similarly centered on sizing suggestions, Perfitly creates avatars based on the measurements consumers enter or their photos, which it says it does within a 97 percent accuracy. Since launching, Perfitly has created avatars for tens of thousands of customers, with the expectation that this will soon grow to the millions.

To create a virtual fitting-room experience for customers, Perfitly translates apparel from its retail partners into 3D smart garments. As shoppers browse an e-commerce site, the solution will suggest the best size for them and show their avatar wearing it, including indicating where it fits tighter or looser. Depending on personal preference, the consumer can then size up or down to get the fit they desire.

“I believe as time goes on, [these] so-called size tags on a garment will become passé, it will not be needed anymore,” said Dave Sharma, co-founder and CEO of Perfitly. “And so all the sizing will have to be on the manufacturing and on the supply side as opposed to on the sales side.”

Based on these avatars in the virtual fitting room, Perfitly can then feed fit information back to brands. For instance, if a garment is too loose on all the avatars on the waist, the company can then adjust its patterns accordingly. As more design work is done in 3D, Perfitly has also created avatars based on dress forms for companies, enabling them to receive the same kind of stress pattern feedback on virtual garments as they are in development. By showing how the garment will fit in a digital format, companies can cut down on the number of physical sample iterations needed.

When it comes to fit, sizing is only half the battle. Consumers have varying body shapes, but design typically only takes certain proportions into consideration, similarly creating pockets of people who struggle to find clothing. “The industry is focused on the hourglass body shape because that’s the dress form, and if you’re going to dress real people, then you have to learn about all the other body shapes,” said Camilla Olson, founder and CEO of Savitude.

Olson was working as a designer when she realized the need to create attire for different shapes. After studying the subject, she identified a total of 729 shapes. The Savitude software asks consumers a handful of questions to get an idea of body shape, such as whether her waistline bends in or out. Savitude also classifies the merchandise on its retail partners’ online shops according to which body types they flatter, creating product pages that only feed the consumer styles that will work best for her.

Learning more about body data has also brought to light the gaps in footwear sizing. Whether due to production decisions or retail buys, shoes are often only offered in one width, which typically serves about 40 percent of the population. Adding two additional widths raises the consumer coverage to 90 percent. Within footwear, another fit failure revolves around the use of unisex shoes, due to the differences between men’s and women’s feet.

“Everyone is using basically the same factors without asking how well this actually fits to the customers,” said Ales Jurca, vice president of footwear research at Volumental. “And now since we have a lot of data on our customers, this gives us the chance to compare the actual foot data with these grading tables, and we can see huge gaps.”

Volumental has completed more than 3 million foot scans, allowing it to not only help consumers find shoes that fit but also aid partners in development. For instance, companies can use average measurements for a particular size to form shoe lasts, or molds, that are more accurate. Another way that Volumental has changed the sizing practice for its partner brands is by helping them to evaluate their prototype fit testers, enabling them to find individuals with feet that represent the average size of the actual target customers.

Learning more about body data can also help companies allocate their sizing selections for different regions and markets. For instance, foot lengths in Asia tend to be smaller than those in Europe and North America. Meanwhile, fit preferences vary from region to region, also necessitating different sizing options.

Through its software, Savitude learns a consumer’s body shape and city of residence. While Savitude does not pass along this data to its retail partners, it uses it to help guide retailers in terms of allocation. “We collect our own database on where distributions of body shapes are, but we keep that proprietary…and we use that to help inform our recommendations on where to make shipments,” Olson said. “Because we know that the distribution of body shapes is different around the world and even in the U.S., tying that in with design is important.”

As fashion companies expand regionally and internationally, knowing these differences can also help adjust size charts to fit customers in new markets.

Making more informed product development decisions helps companies increase the number of consumers that can find clothes and shoes that fit. However, even with increasingly available body data, there are limitations in creating mass-produced apparel and footwear that will fit and please everyone. “Sizing solutions have come a long way and offer a huge benefit to retailers implementing the technology on their e-commerce sites,” Wolf said.

“There will always be individuals who either prefer or require niche clothing options to meet their apparel needs,” Wolf added. “Knowing where these people shop and how many there are can help apparel brands and retailers plan designs and allocate stock more accurately.”