>say a person bought a pair of shoe, sunglasses and a book. For their newsletter, we will show include shoes, sunglasses and books. This was a lot more relevant than sending random stuff.

I agree with the general sentiment of the article, but this seems like a poor example, since a more sophisticated approach can add a lot of value to a recommendation system. How do you know whether a customer is likely to want more than one item in any of those categories? If they already purchased sunglasses, wouldn't they be more likely to purchase, say, a sunglasses case and/or sunscreen? If they purchased a book, do you recommend the same book again? And if not, how do you choose which book(s) to include?

Of course, you could technically still handle this in SQL with a bunch of CASE statements, but obviously that doesn't scale well across a wide range of products. The whole point of ML/AI in that use case is to scale that type of nontrivial decision making.