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How to make body data work

How to make body data work

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How Reebok, Patagonia and G-Star Raw have used body dimension data to their advantage. 

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Reebok CrossFit

Crossfit is one of the fastest-growing fitness regimes in the US, yet until recently there was a dearth of information about the body types of typical crossfit athletes, making fit a challenge. Reebok got around this by using body-dimension data to adapt its crossfit range.

Body-scanning technology took more than 80 measurements from 300 crossfIt athletes, which allowed the brand to identify the average crossfit body shape and size, and how it differs to those of the average gym goer. It created a 3D avatar of its average customer, which was circulated to its design and pattern-making teams.

“We used this information to advise us on where we need to put cut seams, where a garment needs unrestricted movement,” explains Andi Archer, apparel designer at Reebok CrossFit. “We also looked at where we should insert more specific zoning in terms of heat regulation or anti-abrasion protection.”

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Patagonia denim

A brand known for its functionality must offer clothing that fits. For Patagonia’s relaunch of denim in 2015, it used body-dimension data to custom-make its designs to the active lifestyles of its customers. Using research that included consumer feedback and five years’ worth of sales and returns data, Patagonia discovered that its customer was more athletic than the average and designed its autumn 15 range accordingly.

 

 

 

 

 

 

 

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G-Star Raw

When streetwear and denim brand G-Star Raw launched in the Netherlands in 1989, it based its fits on typical European body shapes. Today it is a global brand and combines its existing consumer profiles with body-scan data so it can tweak sizing ranges by region.

“It helped us tackle the challenge of delivering consistent fit globally,” says Ingrid Heijnen, manager of G-Star’s in-house atelier.

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