Stantt Uses Big Data To Size Clothing

The conventional wisdom surrounding clothing is that it fits into three simple categories: small, medium, and large. Stantt, a company that’s looking to meet its funding requirements via Kickstarter, hopes to change that in one of the more interesting applications of big data to a classic problem. Stantt started by 3D body scanning over 1000 men, ranging in age from 25 to 35, and each scan included approximately 200 body measurements. They then developed an algorithm – which extrapolates from chest width, waist width, and arm length, all common, at-home measurements – that determines which of over 50 sizes fits the individual. Though Stantt faces competition from the increasingly common ‘made-to-measure’ online sphere, Stantt is among the first fashion-oriented brand looking to leverage data to improve something we’ve considered standard for years. 

STORY Shifts Retail Concepts

In her talk at the PSFK Conference, Rachel Shechtman described what she termed ‘Retail Media’ with respect to he Brick and Mortar ‘experience,’ STORY. The multi-media concept combines curation and editorial content with more traditional retail structures. Most simply, Shechtman curates her physical locations much like a magazine that changes themes and covers every four to eight weeks. In the same way, Shechtman tells a different STORY in a different location, with a new theme, and ultimately, new products in the same production cycle. This offers a discovery platform combined with content, commerce, and community, that revolves around a physical entity. Recent variations and space utilizations have included Wellness, Color, Making Things, and Art. And ultimately, the consumer doesn’t know what will be next, so they’re constantly enticed back a few weeks later to experience the next story.