When shoppers interact with a fashion retailer, they generate taste-based data. We encourage fashion retailers to retain that intelligence and use it to build a competitive moat around their business.
We help fashion retailers build that type of infrastructure. Because we are certain that the future of fashion will be build on top of it.
Technology We Have Built
If you are a fashion retailer who wants to own the intelligence you help generate, your infrastructure will be composed of these elements:
Fashion Taste Graph for Omnichannel Personalization >
Taste Profiles of your shoppers >
Our unsupervised learning model >
Products We Have Built
Some of the products we’ve built, that you might want to offer as well, are the following:
Smart Virtual Closet Technology >
Outfit Recommender for Physical-Stores >
Recommender – Online discovery – Products & Outfits >
1. Taste Graph. Each fashion retailer will own its Taste Graph, or will end up enriching a 3rd party
This Taste Graph will contain the intelligence generated by shoppers and their interactions with products. A Taste Graph is your unique data asset and engine to build a competitive moat around your business.
2. Taste Profiles. 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. Ontologies will include all fashion concepts, even non-physical clothes descriptors but very relevant when deciding an outfit
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
These toys might look like In-Bedroom Fashion Stylists (watch video), In-Store Recommenders (watch video) or Digital Closets (watch video and read about the tech).
We’ve been analyzing data-based taste for more than 16 years, and specifically in the fashion space for over a decade. This has resulted in more than 25 US granted patents, 6 of them in fashion. We have protected systems and methods to:
· Extract correlations among items in an image (ie: an outfit) and from correlations in a closet;
· Link items in an image to shoppable products (to enable features such as “shop the look”);
·Fashion Taste Graph on top of large amounts of taste data; several use cases of personalization and recommendations.
Chicisimo is led by Gabriel Aldamiz-echevarría, you can contact him at aldamiz@ chicisimo.com.