A new startup uses data and highly tuned algorithms to make both business and fashion decisions — upending conventional wisdom about the need for intuition, creativity and an eye for style.
StyleSeek, a new recommendation website, uses data and constantly refined algorithms to help both fashionistas and the fashion-challenged alike to discover and choose exactly the right thing to wear, at exactly the right time (think work, weekend, night out). But using data to identify fashion choices isn’t to everyone’s taste. Take GQ’s response to the idea of applying analytics to an essentially creative process — one filled with intuition, experience and, of course, an eye for style:
It has to be said that style is and always has been about individuality. This is the main problem that faces the developers of the algorithm — one they are trying to sidestep with … StyleGame, a nine-step profiling quiz that attempts to work out whether you like semi-cutaway collars by flashing up pictures and asking you whether you prefer Daft Punk or Outkast, beach houses or generously appointed New York lofts… Style isn’t really that cut and dry.
With about 50,000 actively registered users — and close to 200 retailers on board, including the likes of Nordstrom, Macy’s and Anthropologie — StyleSeek co-founder and CEO Tyler Spalding (@trspalding) doesn’t quite agree with GQ’s perspective. That’s because conquering the fashion industry is just StyleSeek’s first step in its real mission — to transform e-commerce. The company, which started as a project while Spalding was a student at MIT’s Sloan School of Management, employs 10 people, half of whom are technologists and the other half content curators — or, in Spalding’s words, people who actually understand style “better than I ever will.” Spalding spoke with MIT Sloan Management Review contributing editor Renee Boucher Ferguson about his company’s data-driven mission and its early successes.
What does StyleSeek do?
We’re a new kind of e-commerce personalization platform. Ninety-nine percent of existing e-commerce recommendations are done very inefficiently on a variety of levels. They generally take really, really big data sets and extract patterns and match keywords. What we’ve created is analogous to a Pandora-like system. Each product is mapped by human style experts across a variety of universal characteristics. So we can literally compare a T-shirt and a couch and actually understand the differences in style and what each person likes.