How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach Gabriel Aldamiz... Follow Jan 30, 2018 · 10 min read

Chicisimo’s In-bedroom Fashion Stylist, powered by Alexa.

My name is Gabriel Aldamiz-echevarría, and I’m the CEO and co-founder of Chicisimo. We launched three years ago, our goal was to offer automated outfit advice. Today, with over 4 million women on the app, we want to share how our data and machine learning approach helped us grow. It’s been chaotic but it is now under control.

Our thesis: Outfits & closets are the best assets to understand people’s taste. Understanding taste will transform online fashion

If we wanted to build a human-level tool to offer automated outfit advice, we needed to understand people’s fashion taste. A friend can give us outfit advice because after seeing what we normally wear, she’s learnt our style. How could we build a system that learns fashion taste?

We had previous experience with taste-based projects and a background in machine learning applied to music and other sectors. We saw how a collaborative filtering tool transformed the music industry from blindness to totally understanding people (check out the Audioscrobbler story). It also made life better for those who love music, and created several unicorns along the way.

With this background, we built the following thesis: online fashion will be transformed by a tool that understands taste. Because if you understand taste, you can delight people with relevant content and a meaningful experience. We also thought that “outfits” and “our own personal closets” were the assets that would allow taste to be understood, to learn what people wear the everyday occasions they dress for, and what style each of us like.

Online fashion will be transformed by a tool that understands taste. Because if you understand taste, you can delight people. “Outfits” and “personal closets” are the assets that allows taste to be understood

We decided we were going to build that tool to understand taste. We ended building the infrastructure to automate outfit advice: (i) a consumer app storing the clothes in your closet, and an interface focused on capturing the right input and providing the right output; (ii) a data platform that automates the jobs of interpreting incoming data (taste) and providing the correct output to the delivery mechanisms; (iii) a dataset that reflects what people wear, what people own in their closet, and how people think, when they think about clothes; (iv) and an IP portfolio protecting all of the above.

1st Step: Building the app for people to express their needs

From previous experience building mobile products, even in Symbian back then, we knew it was easy to bring people to an app but difficult to retain them. So we focused on small iterations to learn as fast as possible.

We launched an extremely early alpha of Chicisimo with one key functionality. We launched under another name and in another country. You couldn’t even upload photos… but it allowed us to iterate with real data and get a lot of qualitative input. At some point, we launched the real Chicisimo, and removed this alpha from the App Store.

We spent a long time trying to understand what our true levers of retention were, and what algorithms we needed in order to match content and people.

Three things helped with retention:

Releasing changes into the app.

(a) identify retention levers using behavioral cohorts (we use Mixpanel for this). We run cohorts not only over the actions that people performed, but also over the value they received. This was hard to conceptualize for an app such as Chicisimo. We thought in terms of what specific and measurable value people received, measured it, and run cohorts over those events, and then we were able to iterate over value received, not only over actions people performed. We also defined and removed anti-levers (all those noisy things that distract from the main value) and got all the relevant metrics for different time periods: first session, first day, first week, etc. These super specific metrics allowed us to iterate;

(b) re-think the onboarding process, once we knew the levers of retention. We define it as the process by which new signups find the value of the app as soon as possible, and before we lose them. We clearly articulated to ourselves what needed to happen (what and when). It went something like this: If people don’t do [action] during their first 7 minutes in their first session, they will not come back. So we need to change the experience to make that happen. We also run tons of user-tests with different types of people, and observed how they perceived (or mostly didn’t) the retention lever;

(c) define how we learn. The data approach described above is key, but there is much more than data when building a product people love. In our case, first of all, we think that the what-to-wear problem is a very important one to solve, and we truly respect it. We obsess over understanding the problem, and over understanding how our solution is helping, or not. It’s our way of showing respect.

This leads me to one of the most surprising aspects IMO of building a product: the fact that, regularly, we access new corpuses of knowledge that we did not have before, which help us improve the product significantly. When we’ve obtained these game-changing learnings, it’s always been by focusing on two aspects: how people relate to the problem, and how people relate to the product (the red arrows in the image below). There are a million subtleties that happen in these two relations, and we are building Chicisimo by trying to understand them. Now, we know that at any point there is something important that we don’t know and therefore the question always is: how can we learn… sooner?

This model helps us think. I understand that there are other models and this one might be controversial.

Talking with one of my colleagues, she once told me, “this is not about data, this is about people”. And the truth is, from day one we’ve learnt significantly by having conversations with women about how they relate with the problem, and with solutions. We use several mechanisms: having face to face conversations, reading the emails we get from women without predefined questions, or asking for feedback around specific topics (we now use Typeform and its a great tool for product insight). And then we talk among ourselves and try to articulate the learnings. We also seek external references: we talk with other product people, we play with inspiring apps, and we re-read articles that help us think. This process is what allows us to learn, and then build product and develop technology.