Chicisimo announces today the “Fashion Taste API” to build a Taste Graph for each fashion retailer, and a Taste Profile of each fashion shopper. Read our Manifesto & Learn “Why Now?”
Today we are excited to unveil the Fashion Taste API!
Fashion Taste API builds a Fashion Taste Graph for each fashion retailer, and a Taste Profile of each fashion shopper. It is the technology that will allow retailers to win the heart of people, their data and their pockets. It is the technology that is allowing Spotify, Netflix or Pinterest to understand each user and grow on top of that.
We’ve converted a human problem (understanding fashion taste) into a computational problem.
Technology is opening up lots of options for fashion and there are 4 elements that we consider true building blocks for the Future of Fashion Retail:
1. A TASTE GRAPH FOR EACH RETAILER
- Each fashion retailer will own its Taste Graph like Pinterest does
The Taste Graph of a fashion retailer contains the intelligence generated by shoppers interactions with the retailer products and channels. Think of it as an intelligent data asset which value grows exponentially with any new event, and as an engine that allows you to manage and understand products and shoppers.
2. A TASTE PROFILE OF EACH SHOPPER
- Retailers will build Taste Profiles of each shopper like Spotify or Netflix
A Taste Profile summarizes the taste of an individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases.
3. AN ONTOLOGY INCLUSIVE OF ALL CONCEPTS
- Ontologies will include all fashion concepts and the relations among them, even non-physical clothes descriptors but very relevant when deciding what to buy and why
Each retailer will manage its catalogue and its shoppers with their own ontology.
4. TOYS WILL WIN
- Seemingly unimportant services that look like toys will win the heart of people, their data and their pockets.
Different teams have been trying to build such toys since the early 2000s. We believe there are a couple of organizations out there well positioned to make it. 3 examples we’ve built:
- Watch a 40 seconds video of our In-Store Outfit Recommender;
- Watch a 30 seconds video of our Digital Closet, and read about the enabling technologies;
- In-Bedroom Fashion Stylists, probably the most disruptive of all upcoming toys. We’ll announce it in a few days.
Why is *now* the right timing?
The fashion industry has come a long way since we patented our fashion taste graph in 2013: Machines can now understand fashion products AND fashion shoppers. A human problem (understanding fashion taste) is now a computational problem. This changes everything.
We can now control how to describe products and channels to maximize the information they provide about a shopper when she interacts with them. Also, we can add 3 times more descriptors per product thanks to the Taste Graph and the relations generated.
One of the roles of taste graphs is to assign descriptors to shoppers. Thanks to this automation, a fashion retailer can build a pretty accurate understanding of each shopper, with clean, structured and correlated shopper data.
Clean shopper data is the New Superpower of Product Teams. Fashion ontologies and Taste Graphs are now producing clean, structured and correlated taste data, at exponential rates. Clean Shopper data is the New Superpower of Product Teams: Ambitious and customer focused hybrid teams that are becoming unstoppable.
We’ve created what-to-wear apps with the objective of learning how to understand taste automatically, so we can help people effectively. Via these apps, we have received millions of described outfits with described clothes, the described clothes in millions of closets, and hundreds of millions of what-to-wear queries from people trying to decide what to wear. Our infrastructure and learnings have been built on top of this data and the relations among the data.
If you are a large fashion retailer willing to improve how you manage and understand products and shoppers, please get in touch.
More at Fashion Taste API >.