Sadly, Im here to communicate that weve decided to stop fighting as a standalone company, and we offer ourselves for sale. Weve run out of cash. For the many people whove been supporting us, thanks so much for being there.
Today, we start a different fight: we are going to reach out to potential acquirers. The first step is to share our vision as loudly as possible, describing our assets and our team. Because we still believe.
Weve converted a human problem into a computational problem
In the last 3 years, weve built a taste graph that classifies clothes and understands peoples taste.
Weve converted a human problem (understanding fashion taste) into a computational problem. This has been our focus from day 1, because understanding taste automatically, and being able to act upon it, will be the single biggest breakthrough in fashion ecommerce, we believe.
Whats our situation?
For years, weve been pivoting to solve the above problem. We finally found the right path 36 months ago, but we havent found a relevant business model. We provide more details below. We started applying our technology to our own consumer products under the Chicisimo brand (see videos below), and we just very very recently started offering it as a SaaS via the Fashion Taste API with a strong business model. Weve raised $3,5M (we are extremely thankful to our investors).
If you continue reading, I believe youll find an interesting product, market and team. At least, different from other approaches. I would really like to hear your thoughts after you read all this.
How should we proceed?
Weve been wondering what to do under our situation and we dont think there is a perfect way to proceed. We do know we can be a great fit for other players, so we are going to push as hard as we can:
- Well be reaching out to potential acquirers. This is the list of teams we are contacting or want to contact if you can introduce us to the right people in those companies or other companies, it would help a lot. My email is firstname.lastname@example.org;
- Most importantly, with this post we want to publicly share our vision as loudly as possible. The best way to do it is to describe our assets and our team: our assets are a reflection of what weve considered truly important in fashion ecommerce.
- Any other ideas? This is important for us.
Saying that we build tech that classifies clothes and understands taste might seem a bit abstract. The video above is one of our consumer products and one example of how the tech is delivered to the end consumer (it is just an example, there is much more!). Its an In-Bedroom Fashion Stylist that understands the user and her taste and knows what clothes she has in her closet. It is actually the first time we publicly talk about this product and we just included a reference to it on our site.
#1 Asset: Our fashion ontology
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 couldnt 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;
- 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 wed need. It will allow any acquirer to be 23 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 shoppers 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.
#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.
#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.
Its created by capturing the relations among the retailers 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.
#5 Asset: Our Digital Closet
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. Thats 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.
Weve obtained two key learnings:
- From an architecture of information point of view, a persons 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 persons 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 couldnt 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.
- Adding clothes to your digital closet (one of the mechanisms):
- Our In-Store Outfit Recommender
- Our Digital Closet on iOS. You can install the iOS app directly from the App Store.
- Smart Fitting Room
#6 Asset: Two key patents. Why do we patent?
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 Chicisimos portfolio uncovered market adoption related to linking user-submitted fashion images to shoppable items. Chicisimos patents are expected to provide a competitive advantage.
There is some controversy around patents, so Ill 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; in terms of investment, weve spent a total of $82,120 on all aspects related to IP. Second, as a startup we need to create value and this method has proven successful in the past. Third, weve 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 cant 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 worlds 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 thats been explained above you can read our view of The Old vs The New personalization approaches, here.
Without any doubt, this is obviously our key asset. An already built team with a production learning environment, tools, processes and clean data.
- We are a team of 8. We are 2 full-stack, 1 iOS, 1 Android and 4 product people focused on the ontology, data architecture, interfaces and on understanding what to build. Being a small team is a choice. We are always in search of simplicity while obviously solving complex problems;
- Being remote is a core part of our culture. Our main offices are located at Slack & GitHub & Whereby. We work exceptionally well together and are a bit obsessed with processes. We like having the right tools to do our job, you can see our stack here;
- We absolutely love the problem and feel a strong respect for it. No one of earth knows more than us about the challenge of automating shopping and outfit advice. While we want a machine to solve the problem, we are aware that deciding what to wear and what to buy is emotional, and this fact needs to be embedded into the end product. We talk with many people about how they want to feel when they get dressed, and what they express is emotions. People want to feel confident in their outfits, or comfortable, happy, beautiful, sexy, stylish, powerful whatever definition of well is right for each of them;
- The team has built and shipped the above infrastructure to production with an active community in search of fashion ideas, whose behaviour provides immediate feedback. Our community, which we are grateful for, is a great learning environment, few things beat shipping to production and getting feedback. We have an iOS app, an Alexa Skill, a Google Action and an Android app;
- Our internal data portal. Our job requires a team with complementary skills, and we initially had difficulties understanding each other. We solved this problem by creating an internal data portal that exposes all data and data relations. It is a key asset because it provides a shared language and knowledge where everyone in the team can easily *access and see* the same information.
- Here you have the team bios.
Are you interested?
Please email me at email@example.com.
Remember, we are looking for the right acquirer, and we are very optimistic about what this new journey will bring. Please let us know whats your interest, all emails will be treated with confidentiality.
From the corporate side, we are simple, clean and easy to evaluate. The company does not have tax or litigation issues, and does not lease any property. The company has not acquired, sold, merged, divested, reorged or performed any transaction of such type. The company is incorporated in Spain, has no subsidiaries or holding companies, and owns assets in the US (intellectual property). Its governed by a Board of Directors. We work with exceptional professionals (financial and accounting advisors, corporate lawyers, and VCs) and everything is simple, clean and easy to evaluate.
Some other interesting facts about our journey.
iOS Subscriptions revenue
We tried to monetize in a number of ways, including a subscription service. Going this path actually means a lot of iterations and paying attention to issues different that the problem we are trying to solve.
Although the revenues per install continue increasing, its not enough. This service will end up being free and the owner will benefit by gaining access to the attention, the closet data and the pocket of fashion shoppers.
We exposed our ontology to Google, and we ended up understanding the connection among SEO, conversational interfaces and ontologies
We are in no way a SEO company, but weve devoted some hours to exposing our ontology to Google and to building automatic sentences using different bags of descriptors. Simply by exposing this ontology, and with only one iteration, we multiplied by 3 our KPI (in this case, the KPI is unique mobile users coming from Google).
There is another aspect way more important: weve been able to understand how closely related SEO and rich snippets are to other areas that are very important for our segment: ontologies and conversational interfaces (Google Actions and Alexa Skills). Any work done in these aspects (SEO, rich snippets, ontologies, Google Assistant and Alexa Skills) can have large impact on the others.
Our iOS app: 3 releases each week during 5,5 years
- With an 8-team person doing all of the above, including one person devoted to iOS, we have built and shipped 204 different public releases to the App Store during 5.5 years. We have built 919 pre-production releases, internal, releases. This is an average of 3 releases per week considering no vacations;
- Our rating has always been 5 stars or very close to it, depending on the country. However, it started to decline when we shipped our subscription service. Weve been featured as App of the Day in 140 countries by Apple, many times. Weve been rejected by the App Store many times for many reasons;
- Even todays version of the app is simply a step into its evolution. This week we are releasing new clothes capturing mechanisms: from closet to digital closet to outfit suggestions, in milliseconds.
376 messages per day from our community
Everything has its origin in understanding people. Even those assets apparently far apart from the user, such as the Taste Graph. We care about people:
- In this 5,5 years, weve received 752,000 messages from our community, an average of 376 messages a day. These messages responded to lots of different questions we had, or they were simply general feedback. Weve done tons of user tests, built groups to test 3rd party apps during long periods of time, and more. Now this feedback loop is activated when we need it. The majority of the messages have arrived via Intercom with 472,000 messages (Intercom later became too expensive for our growth), Typeform with 14,600 messages, and via email most of the remaining messages;
- Weve identified retention levers and anti-levers using behavioral cohorts. We run cohorts not only over the actions that people performed, but also over the value they received. Time to convert has been critical for us you can read about all this in How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach;
- Chicisimo is like that friend or mother who helps you decide what to wear, when you have nothing to wear one person once told us. We couldnt be prouder of really helping people.
Hey but does a clothing purchase predict future clothing purchases?
Some people claim that purchasing a product does not always predict future purchases. We totally agree. You might buy today a pair of cowboy boots even if youve never seen a horse before. Or you might have bought a houndstooth coat years ago and never do it again.
However, we are certain that past drivers of purchases do predict future purchases. You can read about our approach to Taste Profiles here.
Last: A Basque song for you
If you read all the way until the end, I appreciate it.
In return, I want you to discover a song in a language youve probably never heard before: Basque. It has a strong meaning for me, and talks about letting go and about resistance.
Dont forget: We are looking for the right acquirer, help us if you can! Can you introduce us to someone in this list? Do you know teams who could be interested?
Im forced to include this Disclaimer: The above text should not be construed in any manner as acquisition or investment advice or a formal offer. Consult your advisers as to legal, business, and other matters concerning any acquisition of Chicisimo or its assets. Any texts or vision described above are for informational purposes only, and should not be relied upon when making any investment or acquisition decision.