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. We simply can’t access people’s closets or understand their outfits, so there is little understanding on Real Fashion on Real People®.
In the following lines, I will share the following:
- Taste graphs will transform fashion;
- They will focus on understanding Real Fashion on Real People®, 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 Real Fashion on Real People®
The learnings below are based on 4 years analyzing the demand of outfit ideas. Analyzing Real Fashion on Real People®, millions of described outfits, the described clothes in millions of closets, and the what-to-wear queries from people trying to decide what to wear for any occasion you can think of.
We’ve focused on two questions related our exclusive are of real fashion on everyday people, what we call real fashion on real people: How do people describe their clothes, outfits and what-to-wear needs? How can we learn about what clothes people have in their closets?
An outfit is a playlist of clothes put together by someone real acting realistically (as opposed to a catwalk, a model, or someone trying to sell us something). But at the same time is much more. It is also a correlated list of descriptors: it can be comfy, or perfect for the weekend. An outfit contains correlations among clothes, and more important, the deep meaning that a person assigns to her clothing preferences. Outfits provide a unique perspective into closets.
Taste graphs will allow teams to own people’s attention
The biggest opportunity in fashion technology today is to build memorable omni-channel experiences, of top of the real fashion of each real individual shopper. The objective of the Fashion Taste API is that fashion retailers can focus on the building side, while easily accessing the clean, structured and correlated taste of each shopper
Traditional tech efforts focus on efficiently selling more clothes to people, without understanding the shopper interests or context. 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.
Real fashion on real people, on mobile, desktop and personal stylists
Spotify has a similar approach. After you listen to music in Spotify, they have a specific profile of you, with your expressed preferences. As you enjoy their services more and more, your taste profiles gets better, and when they recommend you music, it’s like if they’d know you. Well, they do. The same is done by the Fashion Taste API.
Fashion taste graphs 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 of what-to-wear needs
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 has been built to understand how people refer to their what to wear needs and how they describe their outfits. We don’t think it is important to build a taxonomy that describes clothes (there are many teams doing so), so we don’t want to extract 100% of the metadata of a “blue and white striped cotton v-neck shirt with long sleeves”.
The objective of our ontology is to understand people, not to understand clothes. We want to help you decide “how to wear your black dress to go to your friend’s wedding during a cold day”. This ontology allows us to understand people, their behaviour and their needs, and also converts incoming data into clean and structured data, so our algorithms can make use of incoming data. Think of Netflix initial classification system or Google’s synonym matching.
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. It will become easier and easier for a machine to capture and correlate clothes in an outfit, but the real value is to capture correlations as described by people.
Taste graphs capture those correlations among descriptors, outfits and people. Think of it as a brain that understands “what goes well with” any garment for a particular occasion, etc. It has this understanding because it analyzes hundreds of millions of correlated descriptors, described outfits and queries. Then, it filters them to your specific characteristics and context.
The end game. Be true to ourselves
Real fashion on real people will provide true inspiration to each other, and also correlated taste data. Our closets will be inspired by people like us, and everything will change. Real fashion on real people will transform fashion.
Thanks for reading!