I have an interesting anecdote about that. I was consulting for a very large tech company on their advertising product. They essentially wanted an upsell product to sell to advertisers, like a premium offering to increase their reach. My first step is always to establish a baseline by backtesting their algorithm against simple zeroth and first-order estimators. Measuring this is a little bit complicated, but it seemed their targeting was worse than naive-bayes by a large factor, especially with respect to customer conversion. I was a pretty good data scientist, but this company paid their DS people an awful lot of money, so I couldn’t have been the first to actually discover this. The short story is that they didn’t want a better algorithm. They wanted an upsell feature. I started getting a lot of work in advertising, and it took me a number of clients to see a general trend that the advertising business is not interested in delivering ads to the people that want the product. Their real interest is in creating a stratification of product offerings that are all roughly as valuable to the advertiser as the price paid for them. They have to find ways to split up the tranches of conversion probability and sell them all separately, without revealing that this is only possible by selling ad placements that are intentionally not as good as they could be. Note that this is not insider knowledge of actual policy, just common observations from analyzing data at different places.