Our Smart Virtual Closet Technology allows your shoppers to digitally store all their physical clothes, without any friction, in their own smart closet.
Our Smart Virtual Closet Technology increases your shopper engagement. With zero friction, it allows you to:
- 1. Offer outfit advice on how to wear her existing clothes;
- 2. Help shop for new clothes that match her existing needs;
- 3. Help her plan outfits.
Smart Virtual Closet Technology in action
We provide you with the complete infrastructure to fully offer Smart Virtual Closets to your shoppers, with a strong focus on a great, frictionless experience.
- 1. Our Smart Virtual Closet Technology composed of (i) our interfaces to capture clothes; (ii) a Taste Profile for each shopper; and (iii) your Taste Graph;
- 2. Product and outfit recommendations combining their clothes and/or your collection;
- 3. We offer an API and a Dashboard. Our Dashboard helps business teams to fully understand Virtual Closets. In the Dashboard, they view the specific clothes that are being captured, what outfits are being suggested, and participate in the decision making process with a complete understanding and sense of control.
CURIOUS ABOUT OUR TECHNOLOGY?
Continue reading if you’d like to have some details on how the Smart Virtual Closet Technology works. Our Smart Virtual Closet Technology focuses on 3 components:
- Interfaces to capture input;
- Fashion ontology to interpret input;
- Taste Graph to capture and store the intelligence generated by shoppers’ closets, and to deliver personalized content.
ALL STEPS IN THE PROCESS INFORM EACH OTHER
We are fascinated by the efforts required to build the Smart Virtual Closet Technology. Of all the interconnected efforts involved, there are two aspects that we particularly love:
- Interfaces + Data infrastructure. How the interfaces to capture input are so strongly related to the data infrastructure interpreting that input. One cannot evolve without the other;
- Incentives to provide data + Requirements to provide value. How the incentives offered to shoppers to provide their personal data are strongly related to the consequences of having lots of closets created. People offer you their closet data because you give them something in return (normally, outfit ideas). But you can only provide ideas if you have a large dataset of closets.
ONBOARDING USERS TO A SMART VIRTUAL CLOSET
A relevant area of our Smart Virtual Closet Technology has been conceptualized and delivered to the market because of the need to onboard new users to a virtual 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 Smart Virtual Closet Technology 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 Virtual Closet as soon as possible, and before they abandon.
DIGITALLY-POWERED PHYSICAL INNOVATION
This Digital Closet Technology, together with our In-Bedroom Fashion Stylist and the In-Store Outfit Recommender, are the current consumer-facing expressions of our data solutions, our omnichannel retail solutions. All of them are built on top of our Taste Graph. Read our Manifesto on the Future of Fashion Retail.
INTELLIGENCE TO DETECT TRENDS
Depending on your business needs and how you deploy your smart virtual 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.