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Updated November 2, 2022Youβre reading an excerpt of Making Things Think: How AI and Deep Learning Power the Products We Use, by Giuliano Giacaglia. Purchase the book to support the author and the ad-free Holloway reading experience. You get instant digital access, plus future updates.
If you double the number of experiments you do per year, youβre going to double your inventiveness.Jeff Bezos
Stitch Fix, an online clothing retailer started in 2011, provides a glimpse of how some businesses already use machine learning to create more effective solutions in the workplace. The companyβs success in e-commerce reveals how AI and people can work together, with each side focused on its unique strengths.
Stitch Fix believes its algorithms provide the future for designing garments,* and, they have used that technology to bring their products to the market. Customers create an account on Stitch Fixβs website and answer detailed questions regarding things like their size, style preferences, and preferred colors.* The company then sends a clothing shipment to their home. Stitch Fix stores the information of what customers like and what they return.
The significant difference from a traditional e-commerce company is that customers do not choose the shipped items. Stitch Fix, like a conventional retailer, buys and holds its own inventory so that they have a wide stock. Using stored customer information, the company uses a personal stylist to select five items to ship to the customer. The customer tries them on in the comfort of their home, keeps them for a few days, and returns any unwanted items. The entire objective of the company is to excel at personal styling and send people things that they love. They seem to be succeeding, as Stitch Fix has more than 2 million active customers and a market capitalization of more than $2B.
The problem that Stitch Fix has is to select an inventory that matches their customersβ preferences. It does this in a two-stage process. The first step is to gather their customersβ data, information about its inventory, and the feedback that clients leave. Stitch Fix uses this knowledge to create a set of recommendations, using AI software, for what it should send to its clients.
The second step involves personal stylists determining which recommended items to actually send to customers. They also offer styling suggestions, like how to accessorize or wear the pieces, before boxing the items up and shipping them to customers. It is the creative combination of algorithmic prediction and human selection that makes Stitch Fixβs offering successful.
The reason the combination of humans and computers excel at personal styling is that humans are superior at using unstructured data, which is not easily understood by computers, and computers work best with structured data. Structured data includes details such as house prices and features of houses like the number of rooms, bathrooms, etc. Designs of clothes that are popular in a certain year are an example of unstructured data.
Not stopping there, Stitch Fix integrated Pinterest to the process by allowing customers to create boards of images that suit their style. Stitch Fix feeds that information to the customerβs profile, and the algorithm uses that information to more closely match pieces from their inventory. At this point, this information is more useful to the human stylists, but I do not doubt that the algorithms will continue to learn.
Other companies also use machine learning algorithms to recommend what clothes or products users want to see. For example, Bluecore focuses on helping e-commerce companies recommend what is best for their clientβs customers. If a customer visits the Express website, a clothing company, and signs up with an email, Bluecore sees that the customer likes a specific shirt and that customers who like that shirt also like a particular pair of pants. Bluecore allows Express to send personalized emails and ads that contain the best set of products for that customer. The results are astounding for these companies. Customers end up buying much more because the type of clothing that they want to buy is offered directly to them with these personalized results.
Do you ever wonder how Amazon is so good at recommending products that appeal to you or how Facebook ads are (mostly) relevant? Well, they use machine learning to analyze your patterns based on your history as well as what others who looked at the same item as you ultimately bought. Data is continuously captured to make the buying experience better.
Justice cannot be for one side alone, but must be for both.Eleanor Roosevelt
Law seems like a field unlikely to make use of artificial intelligence, but that is far from the truth. In this chapter, I want to show how machine learning impacts the most unlikely of fields. Judicata creates tools to help attorneys draft legal briefs and be more likely to win their cases.
Judges presiding over court cases should rule fairly in disputes for plaintiffs and defendants. A California study, however, showed that judges have a pro-prosecutor bias, meaning they typically rule in favor of the plaintiff. But no two people are equal, and that is, of course, true of judges.* While this bias is a general rule, it is not necessarily true of judges individually.