Do you have a 5-second summary of what you mean by "Growth specifics and positioning of algorithms in the StitchFix S-1"? Just the fact that they use algorithms to tailor individual boxes, or do you mean something more specific?
The way Stitch Fix talks about it in their S-1 makes it seem like the latter is the priority. I'm not yet convinced that the practical value driven by algorithms at Stitch Fix is up to par with how much they talk about it.
I was interested in growth to understand both their growth rate but also to get a feel if it was driven by increasing user acquisition costs like Groupon, Blue Apron, etc or if it was organic.
(which even if you disregard everything else is a really good line to take if you're after recruiting good machine learning engineers!)
(Note: I'm biased and tweetstormed about Stitch Fix's most recent blog post earlier today. https://twitter.com/achompas/status/967085860763193345)
I never tried myself but it seems quite feasible to build a decent profile of someones taste in fashion from a bit of data.
Fast fashion gave us the logistics (no more 9mo from concept to store). But Zara and friends still supply only the major trends. We're still missing for someone to reliably market the "long tail".
I've thought about this as well. How would you personally try to build this profile for users, then market to them based on it?
That could give you an starting point. But I believe the main issue is that fashion products have, by definition, short shelf life, so you can't run the algos on SKU data. Then you can use deep learning on product images + user categorical data to try to predict preferences, maybe simple binary classification?
I guess using images as input should give better features than textual description.
That's part of what makes this industry hard.
You would have to tag the hell out of these photos, right? Disambiguating preferences is the challenge -- the user may have liked images #3 and #7, but why? Specific items, or the color palette, or the silhouette, or just the model? A post by Chicisimo on the front page addresses these hurdles [1].
I've also seen some of these image recognition apps in action -- they pick up on patterns and color very well, but struggled with silhouette.
[1] https://hackernoon.com/how-we-grew-from-0-to-4-million-women...
It seems similar to talking to people at Google in the early days. The thing that they cared about from an engineering point of view seemed weird and they language was alien. 3 years later the rest of the world hits the same problem and I remember the conversations and think "oh so this is what they were talking about".