2018-04-05

Stitch Fix

I had the great privilege of visiting Stitch Fix today, hosted by Dave Spiegel. I was interested in the company for many reasons, but the main one is that it has a large number of PhD astrophysicists on its data-science team. I learned a huge amount while visiting. Here are some random things:

If you are doing data science to inform or support the decision-making of an employee of your company, it is worth spending a lot of computation on that: After all, the employee is very valuable and expensive! On the other hand, if you are doing data science to directly execute commands (for, say warehousing of goods), you better not get it wrong, because if you have a bug, you could literally move lots of stuff where you don't want it!

If you sell clothing, the lead time between buying clothing wholesale and selling it is long! So you can't quickly or in real time feed back customer preferences into your buying choices. That makes prediction of paramount importance! One thing that really surprised me is that Stitch Fix designs and even manufactures some of its own clothing, so they have unique lines of clothing, adapted to their customers' preferences!

And, obviously (but new to me): Clothing is combinatoric! Even in making a standard button-up shirt, there are myriad few-way decisions about collar, buttons, sleeves, relative dimensions, and so on, such that there is no way in the history of all of humankind that you could make every possible (or even every sensible!) version of a standard dress shirt. That puts a data-science-oriented company like Stitch Fix in a very, very interesting position.

1 comment:

  1. David - I’m very curious how StitchFix is using the data to determine my buying habits. Full disclosure - I hate shopping. StitchFix has been my go to ‘buyer’ for the past couple of years. Sometimes the personal shopper gets it right and sometimes not so much. Recently they added a cool Facebook daily quiz which I originally thought would eliminate the personal shopper. It asks me to pick from ~15 items for my preferences. What I’m noticing is that I’m headed back to my boring, unoriginal ways. How will data science manage to keep the personal buyer’s creative touch in my monthly fix? I think I’m as interested in seeing what data science thinks my preferences are as I am at getting new clothes!

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