Menswear Startup Recommends Clothes Based On Your Spotify Data
Look, I’m not going to pretend to be some fashion expert here. Father John Misty would probably dismiss my wardrobe as graphic-designer quality at best. So maybe I could benefit from a service like Eison Triple Thread, if I felt comfortable being pigeonholed by an algorithm.
As Racked points out, the custom menswear company, which counts NBA players Steph Curry and Damian Jones as clients, is debuting an app this week that will recommend clothes based on your Spotify data. It’s called FITS, and it promises to “build a premium made-to-measure wardrobe based on your lifestyle and musical preferences.” After pairing the app with your Spotify account, you take a lifestyle quiz, upload your measurements, rank trending fashion looks, and the service suggests clothes based on your activity. And voila, you suddenly become the physical embodiment of your favorite tunes?
Founder Julian Eison’s explanation of the service feels a bit on-the-nose: “A guy who was born between 1984 and 1988, likes hip-hop, and works in tech in San Francisco will probably like clothing that’s on trend, and so we’ll feed him looks based on that demographic and see what he responds to. If someone else likes upbeat music, was born in the ’80s, and listens to music from that time, we can gauge that his style is probably similar to Joey [from Friends]. People who listen to ’60s music like the Beatles will have suggestions like high-rise jeans and corduroy.”
Racked adds that, per Eison, Drake fans will likely be served photos of streetwear such as fitted tees and velour sweatpants, while Lionel Richie listeners might get red ribbed sweaters and blue jeans. However, Eison insists it’s not as simple as, “Drake wears this, so buy that.” That’s a relief. Still, how will Eison synthesize the taste of a guy like Curry, which extends from Paramore to E-40? People are complicated! And what if you’re way into, like, Boredoms? I’m guessing not many people who sign up for this service will have that problem, at least.