
How we grew from 0 to 4M women on our app, with vertical machine learning - aldamiz
https://medium.com/@aldamiz/how-we-grew-from-0-to-4-million-women-on-our-fashion-app-with-a-vertical-machine-learning-approach-f8b7fc0a89d7
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dang
I know you're just trying to get attention for some good work, but promotional
voting and—especially—promotional commenting are the sort of thing that drive
HN readers crazy and cause them to use unkind words like 'shill' and 'spam'.
The HN community smells these things like a truffle pig smells truffles and
they regard it as manipulating the system here. So it's definitely not in your
interests to do this! In practice that means you should get your friends and
team-mates to _not_ vote and especially not to post booster comments in the
thread.

Since it looks like the underlying work here is good, I suggest waiting a week
or two and then reposting it, and make the title less buzzwordy and more
neutral. If you email us at hn@ycombinator.com when the post is up, we can
make sure it doesn't get flagged.

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aldamiz
Thanks for the heads up, dang.

Truth is: for the first time ever, we've decided to talk about our machine
learning approach and how we've managed to build our project, and we've
thought many of the lessons learnt have value for the community.

We've really worked hard on the content of the post, trying to offer valuable
insight on a number of things: how we work retention, onboarding, what's our
learning process, and most importantly how we understand fashion taste. Lots
of tips that imo are valuable for the community. And all this work, we've
wanted to share it here on HN. Our friends are as excited as we are, and some
have asked for questions / congrat'ed us here. No questions were planned.

About the title, it's what we've managed to do! While others spend millions on
acquisition, we haven't, it's been product based, no tricks, its been done by
building an efficient product, step by step, countless nights. Few people in
this industry, outside the fashion space, know Chicisimo, and we wanted this
to come to an end. I'd appreciate the flag to be lifted.

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xavi
Is all the tagging manual?

I assume that in order to find images of relevant outfits for the expressed
"needs", the images have to be tagged with colors, brands, garment types... If
that tagging is manual, I wonder if it could be automated using the object
detection feature of an image analysis service like Amazon Rekognition or
Google Cloud Vision.

Maybe automated tagging would allow richer tagging, and that could be key to
find the best results for each user's taste.

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furilo
How much have you spend on Mixpanel??? ;)

How did you get to insights/assumptions like "If people don’t do [action]
during their first 7 minutes in their first session, they will not come back"?

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aldamiz
Thanks for the question furilo.

We run behavioral cohorts. We do cohorts over everything, every action people
do, every screen they visit, every piece of value they receive. We also know
when those actions take place (first 5 minutes, first hour, day, etc).

With the above, we've learnt what actions have the strongest retention. Now we
need to look at the duration of the first session, which we have. We know that
if we dont convert people during their first session, they is no coming back -
no push or email or anything, will bring them back.

Once we know what the lever of retention is/are, and much time we have to
convert, we run user tests with different types of users (converted, non-
converted, people who've abandoned the app, people who dont know the app), and
observe how they discover the lever, how they describe it, how they use it.
This is gold, and helps us iterate.

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soci
Which action has the strongest retention in a fashion app like yours?

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abarrera
Great job guys! Been following for a long time and it's impressive how far
you've taken the company during this time! Kudos and great article!!

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aldamiz
thanks abarrera :)

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soci
Article suggests they are using perceived value of an outfit to train their
Machine Learning model. I wonder what are those actions, Likes? Add to cart?

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aldamiz
Thanks for the comment soci.

There is a lot of actions that express taste - sharing your own tagged
outfits, searching for something, creating a specific album, and many more.
And actually, each expression of taste has its own value. As we move forward,
we'll need to get smarter as how we can help people, based on what data.

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quarizmi
Fantastic article and way to go on the accomplishments. Looks like you are in
an amazing space to be. I am a big big fun of recomender systems.

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aldamiz
thanks quarizmi!

Lets see how recommender systems evolve in this space. The tech and
implementation will need to be very different to traditional recommenders, and
access to data will be a huge barrier imho. We'll see:)

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michokest
Thanks for sharing! You’ve talked about your ontology, can you explain a bit
more about how you build it?

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aldamiz
Thanks for the question.

The question we figured out we need to answer is: what type of outfit ideas
are people looking for, and how do they describe their needs? And then, how
can we match those needs with relevant content.

So what we did (what we do) is look at people's queries, and also at how
people describe their clothes, their outfits or albums (combinations of
outfits, like pinterest boards). There is a lot of data here, millions of
keywords, but many of them are similar (pants and trousers, or pantalones in
Spanish). So we’ve extracted the main concepts (pants) and built a system of
equivalences. Basically, now, we know “all” the needs people have when it
comes to deciding what to wear, and have a pretty complete view of different
ways of describing those needs, and how good the system is at responding to
those needs (database of outfits).

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gimenete
Very interesting article. It's impressive the engineering effort you are
making here.

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aldamiz
Thanks gimenete! We actually do things as simple as possible. But complexity
is increasing:)

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adrianmg
Awesome article with tons of learnings and details <3\. I'm a big fan of them.

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pab_rey
Amazin job... thanks for sharing!!

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aldamiz
thanks pab_rey!

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jordimirobruix
Amazing work!

