Our technology to understand shoppers, and provide a truly omnichannel personalization experience
We patented our Taste Graph Technology in 2013.
We've perfected it since then.
Through our consumer apps, we have received millions of described outfits, the described clothes in millions of closets, and hundreds of millions of what-to-wear queries from people trying to decide what to wear.
In 2013, with so much consumer data we built a Taste Profile for each person, and a Taste Graph to serve the right products to each individual based on her specific interests. It is important to note that a profile in fashion is not only driven by taste, but also by other triggers. You can read about it in the Taste Profiles section.
That's the origin of our technology. The Taste Graph Technology receives data and correlations. It also assigns the correct descriptors to clothes, outfits and to people. It is the tool on top of which teams can effectively work in personalization. Delivered via a website, in a physical store, or delivered via Alexa in a bedroom or closet.
Our Fashion Taste Graph Technology for Omnichannel Personalization is the engine that allows us to assign descriptors to shoppers. It allows us to understand the taste of each shopper, and provide them with personalized omnichannel recommendations.
It is the infrastructure and data that contains, manages and understands the taste of each individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases. The behaviour of each individual, the aggregated behaviour of everyone, and the behaviour of any subset we'd want to target.
The backbone of our Taste Graph is our fashion ontology, which you can read about here.
We build your Taste Graph, your tool to understand the taste of each individual shopper.
During the last 20 years, we've solved specific problems in car manufacturing, music and finance by introducing technologies to understand specific behaviours and automating very concrete processes.
Our consumer fashion apps bring unique impact to our learning process.
They allow us to see the future with more clarity, by solving the problem end to end and receiving direct feedback from the end-consumer. Iterating a consumer app is a unique learning experience, and it contributes in a great way to the definition of the Taste Graph and of course of the Ontology. You can read about or approach to growth in our consumer product in this Medium essay.
Then, we from Using that data to build an automated personalization engine lead us to build our Taste Graph, and patent it in 2013. It has proven to be a unique personalization omnichannel technology, and a great internal tool for classification and categorization of clothes.