The Fashion Taste API algorithms mix and match the clothes of the shopper by using the intelligence generated in its Taste Graph, and filtering by the taste of the shopper, as previously defined by the Taste Profile created of her.
A Taste Graph is the tool to understand the taste of each individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases. 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.
You can see a version of this digital closet technology by installing the Chicisimo iOS app.
The video above shows how a shopper is using her digital closet in an iPhone app, and how she is selecting one of her clothes. After doing that selection, the smart wardrobe offers her three services:
First, she sees the other clothes, so she can choose a second garment to obtain the results that we describe below;
Second, she sees complete outfits or ensambles with the first -or the two first- garment she’s selected, and the rest of the clothes in her closet;
Third, the digital closet shows how other women are wearing clothes similar to the one in her selection.
The technology to build this has been developed by the Fashion Taste API:
We learnt that two important questions were: How do people describe their clothes, outfits and what-to-wear needs? What clothes do they have in their closets and how do they wear them? In order to respond to these questions, we built a fashion ontology and a taste graph, and all the infrastructure to automate outfit advice.
An outfit is aplaylist of clothes. It is also a correlated list of descriptors: it can be comfy, or perfect for the weekend. An outfit contains correlations among clothes, and the deep meaning that a person assigns to her clothing preferences. Outfits provide a unique perspective into closets.
Taste graphs bring a unique opportunity to own the fashion space
The biggest opportunity in fashion technology today is to build a mechanism to understand post-purchase clothing behaviour. And then, build technology on top of that understanding: technology to help people feel better with their clothes.
Traditional tech efforts focus on efficiently selling more clothes to people, and ignore the post-purchase experience. Once the purchase is finished, companies are blind and can’t see what happens next.
Offering a post-purchase experience that helps people feel well with their clothes, will let the winner own people’s attention, and so many more things as a result.
Taste graphs will power a Spotify for fashion
A Spotify for fashion will understand your taste and needs, because it’ll be powered by a taste graph.
It will help you decide what to wear at any time. You’ll be able to easily store your clothes in a virtual closet, and it will put outfits together for you. It will help you plan your outfits depending on your context, and will suggest new clothes that match your wardrobe.
Helping people feel well with their clothes will be the key functionality of such a service. People want to feel well with their outfits. They want to feel confident, comfortable, happy, beautiful, unique, sexy, stylish, powerful. Instead of that, many people feel stressed or bored or tiny. More than about clothes, it’s about wellness.
1.- Capture units of taste data
Before we try to understand taste, we need to understand what type of data we need to focus on. Spotify focuses mostly on playcounts (each time you listen to a song), and a playcount clearly defines your current behaviour.
We have learnt that the units of capturable taste data are related to text and images. Words express a need (“i need ideas to go to the office”). Images ofclothes represent the clothes people own, and need help with. There are other units of capturable taste data, but it comes down to text and images. Then, in our mobile app we’ve built different easy-to-use input interfaces to capture data and allow people to communicate with the system. You can also see our In-Bedroom Fashion Stylist and our Digital Closet technology.
2.- An Ontology to understand fashion data
But fashion has a problem: it lacks a common classification system. The expression of clothing behaviour is very fragmented: text and images have different meanings for each person, and each person expresses the same concept differently. Due to the lack of this classification (or taxonomy), people’s data is noisy and algorithms cannot work with it. To solve this problem, we’ve built a fashion ontology, which is the backbone of our taste graph.
Our ontology gives structure and meaning to the incoming data. It allows us to interpret data. It is a multilevel “list” of hundreds of thousands of unique ways to describe what-to-wear needs. Think of Netflix initial classification system or Google’s synonym matching.
A derivative of our main ontology is our ontology of meta-garments, abstractions of specific garments. These meta-garments are the result of another learning: only certain attributes of a garment are relevant when solving the problem at hand. This ontology is 100% user-driven, it’s been built from the bottom up, and it is the result of the need to help people with their outfit needs.
3.- Taste graphs to understand fashion taste
When we get dressed in the mornings, we establish correlations among clothes, and among our ways of describing our outfits and needs. Look at yourself today: you are wearing a playlist of correlated descriptors and clothes. You’ve built an offline taste graph.
Taste graphs capture those correlations among descriptors, outfits and people. Think of it as a brain that understands “what goes well with” any garment, or for any occasion, etc. It has this understanding because it analyzes hundreds of millions of correlations, outfits and queries. Then, it filters them to your specific characteristics and context.
Our taste graph allows us to respond with output to any input. We call it the Social Fashion Graph and we patented it back in 2012. You might think that the image below is super simple, but that’s how simple we want the system to be: receive input > produce output.
The end game
Taste graphs will provide structured and correlated taste data. And then will allow teams to build personalized and meaningful services for each person. Our closets will be taste graphs connected to ecommerces catalogues (also graphs), and everything will change. Taste graphs will transform fashion. Head over to the Fashion Taste API to learn more.
The largest opportunity is fashion technology today is to focus on helping people feel better with their clothes. To do so, we need to understand fashion taste. Once we achieve that, we will be able to build personalized experiences on top of people’s unique taste.
But understanding fashion taste and clothing behaviour at scale is a difficult challenge. That’s our focus at the Fashion Taste API. We are building the infrastructure to automate outfit advice. This infrastructure includes four assets:
1.- A fashion app that has virtual closet. The app helps women as an outfit planning app and Apple features regularly features as App of the Day world-wide. The app helps us learn outfit and clothes behaviour;
2.- A taste graph that is in charge of structuring fashion data, and assigning the right descriptors to outfits and to people. In fashion technology, obtaining clean data is not easy because of the lack of a fashion taxonomy. We’ve built a fashion ontology, and we’ve embedded it into the taste graph that helps us solve this problem. The Social Fashion Graph, that’s how we call our taste graph;
3.- A data portal. This portal provides transparency to the team, and ease of access to data;
4.- Fashion technology patents. These patents protect innovations in three fields: image-based shopping, outfit and closet data, and outfits search.
Are you interested in fashion technology? Please read more about the Fashion Taste API.
We are looking for a Growth designer, someone oriented to scaling the business while designing for people;
You have experience in visual design, interaction, product thinking and user insight, or you have a strong desire to work developing those four skills;
You are a team player. You need to be able to communicate very well with your colleagues, and have experience working with iOS and Android teammates.
Our culture and work environment
We have a purpose. We strongly believe in what we are doing and we think that an app such as Chicisimo must exist. Read about our purpose, below.
We are data obsessed. While you work at Chicisimo, you will be oriented to growing the business: you will have a clear understanding of the levers of conversion, and how different processes influence behaviour and growth. You will also have a clear view of the speed of iteration allowed by each area of the app. You will seek to beat specific metrics, and each piece of work you do will be based on information and will provide information after being shipped.
We ? tools and processes. You will work with the right tools. We are also a little bit obsessed with processes. Simply put, we like having all we need to do our job. We want your input shaping the way we work and the tools we use.
We are remote. Being remote is a core part of our culture. Our main offices are located at Slack & GitHub & Appear, and we feel extremely productive and organized. Also, this is a work environment with a strong communication within the team. Being remote means that you are very disciplined, and also brings the freedom of working from anywhere you want.
Don’t like fashion? Don’t worry, you don’t need to like fashion. You need to loooove interaction design, and the processes around it.
First weeks: As a growth designer, you will be working on processes to influence behaviour and increase conversion rates. You will focus on two specific growth-related processes within the app, and you will ship designs against specific metrics. You’ll see the first results;
First couple of months: You will feel you have a clearer understanding of our growth engine, and how to influence it;
By your first year: You will dominate the process of onboarding people; you will understand how a subscription app grows; you will understand how to identify levers of retention and how to influence them. You will speak metrics.
What are we offering?
We are offering a unique opportunity to have impact on people, and produce such impact developing your own ideas, being based anywhere you want;
We offer a key role at a winning company in a key sector that is going to be radically transformed by taste-capturing technology;
We will provide you with everything you need to feel well and do great as a growth designer. You will work with a focused team in a great environment focused on making you, and all of us, better;
Obviously, we will offer you a competitive salary.
Chicisimo has a purpose
We’ve come to the conclusion that the single biggest opportunity in the fashion space is yet to be realized, and it’s about helping people feel better with their clothes. Today, most technology efforts are focused on selling you more clothes. Focusing directly on that, in our opinion, means focusing on the small opportunity.
The big opportunity in fashion technology today is to build a mechanism to understand people, then build technology on top of that understanding, and then use that technology to help people feel better with their clothes, by becoming their personal stylist and personal shopper. Offering such an experience will let the winner own people’s attention, and so many more things as a result. That’s where we are focused, and that’s our purpose. And it’s all about people, their emotions and taste:
Emotions. When we ask people how they want to feel when they get dressed, they express emotions and a search for wellness. People want to feel confident in their outfits, comfortable, happy, beautiful, sexy, stylish, powerful…;
Taste. Understanding the specific clothing behaviour of each individual is one of the major challenges of fashion technology today. At some point, a few tech players will understand our clothes behaviour exactly as Spotify understands our music behaviour. That’s our specific focus here at Chicisimo: so far we’ve learnt what are the meaningful units of capturable what-to-wear data, how to build interfaces to capture those units, and how to interpret them.
Get in touch with us
This is what motivates us. If you want to contribute, please get in touch with us. When you do, tell us what motivates you, and give us a few examples of how your design work has impacted metrics in your former projects.
You can email Gabi at aldamiz@chicisimo dot com, or simply call at +34 666 552 418.
About a year ago, we shared our approach and our vision on how we’ve applied machine learning to helping women plan their outfits and use a virtual closet. We didn’t have a blog back then, so we shared our thoughts on Medium. Now, we want to link to it from here.
In that post, we focused on the following aspects: