Aagor Weaves
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 app 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: