Our assets:

#1 Asset: Our fashion ontology

Ontology of fashion products. Because fashion lacks a standard to classify clothes.

Fashion lacks a standard to classify clothes or to refer to the variety of concepts that describe products, styles, and personal fashion preferences. When we found ourselves receiving millions of outfits, clothes, queries and related input, we only saw unorganized data, so chaotic that it was impossible to understand, manage or build on top of it. Our users could not really express their needs precisely, we could not describe our content in a way that could be found by those in need of it, we couldn’t even do a good job at categorizing our own content.

The situation above was the origin of our ontology. Today, our ontology understands any incoming input, cleans and structures incoming data, and converts unorganized data into data that a machine can perfectly work with.

We consider our ontology as the backbone of our Taste Graph technology. We divide our ontology into two parts:

Products ontology . It is a 5-level ontology that describes products and subjective characteristics of products. Learn more here;

. It is a 5-level ontology that describes products and subjective characteristics of products. Learn more here; Outfits ontology. It is a 2-level ontology that describes outfits, mostly with subjective descriptors.

#2 Asset: A system to “classify clothes” automatically, with 175 million classified and correlated meta-products. This system allows us to automatically understand, manage and act upon any collection from any retailer (similarities, correlations, recommendations…)

This is the key asset that we have built. If we were to start from zero, this is the asset we’d need. It will allow any acquirer to be 2–3 years ahead of the rest.

To explain it easily, this system is like a brain that understands clothes and outfits, and allows you to organize and display products at your convenience, or your shopper’s convenience. It was constructed after analyzing millions of perfectly described outfits and fashion products uploaded to our system by different subsets of users of Chicisimo, and after analyzing how people interact with them.

The system converts fashion products into meta-products, which are abstractions of specific products of any catalog or closet. A fashion product is ephemeral, but its descriptors are not, so the system retains the value.

A meta-product is the most basic yet relevant description of a product, and one of the first tasks of our infrastructure is to convert any incoming fashion product into a meta-product. While a person might see a given garment, our system reads a set of descriptors, for example: burgundy + sweater + v-neck + comfy + casual + for school + size 42 + cashmere, etc.

For any given retailer, this system can automatically digest its catalogue and then, automatically: (i) Understand each product; (ii) Identify missing information; (iii) Identify similar products, defining similarity in a number of ways; (iv) Build complete looks mixing and matching the clothes in the collection; (v) Identify products that make sense to display together; (vi) Recreate any outfit with garments of the catalogue; (vii) display the correct products for each shopper, or for the current interest of each shopper; (viii) If the system detects a product that it cannot understand, it isolates the descriptor and incorporates it into the ontology if the team so wishes.

A system to “classify clothes” automatically. Allows you to display products at your convenience, or your shopper’s convenience.

#3 Asset: A system to “understand people”, that builds a Taste Profile of each shopper based on the interactions of that shopper with fashion products and the retailer channels

It is fascinating how easy it is to understand people, once you come up with the right approach, and you have the two assets above. Our approach is similar to what Spotify or Netflix do. Luckily for Spotify, music has a universally accepted classification system. Netflix simply did an exceptional job at building their classification system.

In our system, any action performed by the shopper implies an interaction with products or with sections of a channel (a website…). As the products and channel sections are perfectly described and structured, user interaction generates precise information about your shopper. This information is registered and organized, creating the shopper taste profile.

Creating taste profiles, the retailer understands each shopper, and can automatically adapt the shopper experience to her taste and needs in infinite ways. You can read about our system to build Taste Profiles here.

Spotify, Netflix and the Fashion Taste API. Companies building Taste Profiles.

#4 Asset: One Fashion Taste Graph for each retailer

The Fashion Taste Graph of a retailer is, again, a brain. Like the brain of the “Chief Stylist” who knows precisely each product and shopper and the retailer editorial line.

It’s created by capturing the relations among the retailer’s products (garments and outfits), shoppers’ behaviour and descriptors. It improves exponentially with any new data point that it captures. It learns from any new action. And is able to assign new descriptors to any product or people based on previous learnings. Any relevant customer interaction in the future, will be built on top of a Taste Graph. Learn about it here.

Interestingly, lots of retailers rely on editorial teams to organize and display very large collections of products. These teams who do not trust machines to do the job, and we understand the reasoning hind that. We think that they are producing a unique editorial intelligence that they are letting go, and it should be retained. Read why and how that is done.

The first iterations of our Graph got the basic concepts right, but their understanding of shoppers and retailers was very poor. As a result, they were useless. In the image above, you can see a representation of our 2012 Taste Graph, it was pretty useless.

#5 Asset: Our Digital Closet

Once you have the data architecture and the interfaces in place, a digital closet is a simple concept.

It comes a point in time when your impact as a team increases exponentially. In our case, that moment arrived when we started to have clean correlated data and we could operate it effectively. That’s the moment we are at: with the ability to build at exponential rates.

Our digital closet is an example of building on top of the right assets, and you can see it in the videos below or you can download the apps or skill. Our Digital Closet Tech allows your shoppers to digitally store all their physical clothes, without any friction (the clothes they bought on your site, and the clothes they bought in other fashion retailers). It allows a retailer to help shoppers decide what to wear and what to buy, and it is really engaging.

We’ve obtained two key learnings:

From an architecture of information point of view, a person’s closet is exactly the same as the collection of any retailer. This realization had two major consequences for us: (i) we could remove a lot of code and we love that; and (ii) any work we do on fashion products is applicable both to a retailer collection and to a person’s closet; Building digital closet tech requires interconnected efforts from very different disciplines. For example, it is fascinating how strong is the relation between the “closet interfaces” and the “data architecture”. Without the team structure we have, we couldn’t have built this tech. If you like complex interconnected software problems, I encourage you to read our description of the digital closet tech, here.

A few applications:

View of the magical In-Bedroom Fashion Stylist: 100% built on top of the above infrastructure, you can install it from the Alexa Skills Store.

Our In-Bedroom Fashion Stylist — learn more here

Adding clothes to your digital closet (one of the mechanisms):

Adding a skirt to your Digital Closet, using QR.

Our In-Store Outfit Recommender

Our In-Store Outfit Recommender — learn more here

Our Digital Closet on iOS. You can install the iOS app directly from the App Store.

Our Digital Closet Tech — learn more here

Smart Fitting Room

Our Smart Fitting Room — learn more here.

#6 Asset: Two key patents. Why do we patent?

Chicisimo owns foundational patents in the online fashion market being infringed by one of the GAFA companies, and overcoming Facebook’s social graph. You can read about it, and the 5 key highlights of our patents, here.

Why? When we started looking into fashion taste, we considered that there were 3 processes we wanted to own: (i) mechanisms to capture taste inputs; (ii) systems to interpret input; and (iii) system to automatically match an item in an image, against its equivalent in a database with ecommerce links to purchase that same product. We patented the second and third processes.

An independent review of Chicisimo’s portfolio «uncovered market adoption related to linking user-submitted fashion images to shoppable items. Chicisimo’s patents are expected to provide a competitive advantage». We’ve also protected a taste graph based on correlations among fashion images (and inside of them), as opposed to FB’s social graph using similarities among users.

There is some controversy around patents, so I’ll clarify our position. First, we have a lot of experience in this field and this has absolutely never been a distraction in terms of time. Second, as a startup we need to create value and this method has proven successful in the past. Third, we’ve never thought of using patents against others. And now, our number one driver for building IP: companies sometimes need leverage to negotiate or deal with the big tech players. Even though a startup can’t get into an IP fight with a large co, our patents do provide and will provide that leverage.

#7 Asset: Our SaaS to build One Taste Graph for each Fashion Retailer

Our SaaS solution, Fashion Taste API, is in its early days. It offers to build one Taste Graph to each retailer. We are in very specific conversations with some of the world’s largest fashion retailers to offer them our software as a service, and help them display their products to their shoppers. IMO this is an opportunity for many players, ask about it if you are interested.

This is an interesting segment with an incorrect focus in my personal opinion. Most personalization players trying it in fashion are failing. The traditional approach focuses on capturing the relation among two nodes (i) without being able to understand the meaning of the node, and (ii) without being able to understand past purchase drivers of the shopper. In a world with ephemeral content and with no relevant ontology, this has proven not to work.

Our approach is different. Hopefully that’s been explained above — you can read our view of The Old vs The New personalization approaches, here.

Old vs New personalization approaches. Continue reading here >

Team