Learn how to capture the clothes in your shoppers' closets, without friction
Our Digital Closet Tech allows your shoppers to digitally store all their physical clothes, without any friction:
Our Digital Closet Tech increases your shopper engagement. With zero friction, it allows you to:
These services can be provided via your traditional website or your mobile apps, or via new omnichannel retail experiences such as an In-Bedroom Fashion Stylist, an In-Store Outfit Recommender, or a Smart Fitting Room.
We provide you with the complete infrastructure to fully offer Digital Closets to your shoppers, with a strong focus on a great, frictionless experience.
Our Digital Closet Technology focuses on 3 components:
We are fascinated by the efforts required to build the Digital Closet Technology. Of all the interconnected efforts involved, there are two aspects that we particularly love:
A relevant area of our Digital Closet Technology has been conceptualized and delivered to the market because of the need to onboard new users to a digital closet, without friction.
When a new user opens an app (or any other interface) for the first time, she needs to be onboarded in a way that she sees value in seconds. Under no circumstances someone is going to upload her closet if the system is complicated. With that objective, we looked very carefully at the query data of our own consumer apps, and shipped lots of incremental iterations of our capturing interfaces and our data infrastructure.
The result is a very effective Digital Closet interface and a very sophisticated data infrastructure to onboard users to a Digital Closet.
We define onboarding as the process by which new users find the value of the Digital Closet as soon as possible, and before they abandon. Our Digital Closet Technology has been built thanks to our Vertical Machine Learning approach. We wrote about it, and about our approach to learning, in this essay here.
This Digital Closet Technology, together with our In-Bedroom Fashion Stylist, the Smart Fitting Room and the In-Store Outfit Recommender, are the 4 current consumer-facing expressions of our data solutions, our omnichannel retail solutions. All of them are built on top of our Taste Graph.
Depending on your business needs and how you deploy your digital closet, using the Taste Graph might help you to isolate early adopters of third party trends.
We are looking for partners to deploy our technology to a small subset of a few thousand shoppers in Seoul, Tokyo and Rio. Then, do a close follow up of the 3rd party brands they buy, and analyze how early-stage collections evolve to mainstream or not. This would become an automated system to find people who detect trends, early.
We built a similar system for songs, back in 2006, which was later acquired by Apple for Apple Music. It was an anomaly detection system that identified popular songs and traced them back to their early beginnings. There, it identified who were its first listeners. The behaviour of "first listeners" was used to inform of potentially future trends.