Technology of Choice and Defining Selections

Though I have never worked in the apparel space.  I have worked in spaces where consumers make many choices in context, and companies aim to insert new choices to maximize engagement, while strengthening demand by marketing influence.

I happened on this article in Stitch Fix,  an online personal styling service in the United States, talking about their use of technology.

Some fascinating things here. both decision oriented and mathematically defined.  Algorithms of choice.  Useful beyond the realm of apparel selection?  I think so:

In their Multithreaded Blog:

WELCOME TO Stitch Fix  (If you read the whole thing at the link below the math is covered)

We are reinventing the retail industry through innovative technology.

Simulacra and Selection: Styling at Stitch Fix ... 

Modern retailers aid and influence customer decisions, using techniques like recommender systems and market basket analysis to deliver personalized and contextual item suggestions. While such methods typically just augment a traditional browsing experience, Stitch Fix goes a step further by exclusively delivering curated selections of items, via algorithmically-assisted stylist recommendations1.

For most of the history of Stitch Fix, stylists have worked with a styling platform that functions in a fairly straightforward way. For simplicity and concreteness, imagine an e-commerce platform with various filters, wherein stylists are able to browse for clothing items and add them to a cart. The role of our styling algorithm in this system is to rank the items based on information the client provides us, and the role of the stylist is to select an assortment of items that a client will love.

I’m going to gloss over all the details of the styling algorithm except for one important and nuanced point: it is trained to estimate the probability that a particular client will like a given item if a stylist decides to send it. This is a natural and useful framing: we observe what happens to items that stylists select, but not the counterfactual for items they don’t select. However, this selection bias comes with some occasionally perplexing caveats. 

One observation of long-standing Stitch Fix lore is the “shorts in winter” problem: the styling algorithm tends to assign a high score to shorts during the dead of winter. Do we somehow think that everyone wants to stock up on shorts despite the chilly weather? Of course not: this only means that shorts are likely to be successful if a stylist chooses to send them, which they won’t do without a very good reason—e.g., a client requesting them for a tropical cruise. This is an amusing example but the problem is broader: stylists need to spend a decent fraction of their time browsing through items that, for one reason or another, are clearly a poor match. .... "