There is plenty of data in the manufacturing and distribution of clothes. But once clothes are sold and people are wearing them, there is nothing. No data. Nada.
In the following lines, I will share the following:
- Taste graphs will transform fashion;
- They will focus on understanding post-purchase clothing behaviour;
- They will allow tech companies to understand taste, as Spotify does with music;
- They will end up owning people’s attention, because they will be useful.
Analyzing demand for outfits
The learnings below are based on 4 years analyzing the demand of outfit ideas at Chicisimo, where we develop personal stylist technology.
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 a playlist 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 of clothes 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.
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.
Thanks for reading! 💖