Take this simplified example. Say that you want to predict whether a driver will cause a car accident. You could run the stats and say that poorer, older, less educated, alcohol-impeded, sleep deprived drivers statistically cause more crashes, and then take an 80-year-old high school graduate with an income of $20K/year and say "He's three of those five categories, that makes his risk higher." Or you could observe footage of every minute of him driving, count the number of times he strays out of lane, turns without his blinker, doesn't look at the road, speeds, runs a red light, etc. Which is going to give you a more accurate picture?
Marketers build up demographic profiles because historically, that's all the information they have had available to them. The detailed record of everything their customer has ever done has been impossible to collect, or illegal for privacy reasons. Big Tech has that record. And they can use it to make much more accurate machine predictions about what a person will do than demographics alone can predict.
In your first post you said that computed statistics are there "to make advertisers feel better about themselves". I pointed out that those computed statistics are still very valuable, even if they're based on probabilities and not on tangible data. Of course that with more real-world data the statistics are more accurate, but the reality is that real data is likely unavailable for most users. If the only available data are a few pictures and behavioral records (what they liked, who they follow, etc.), then those computed statistics are still much better than nothing.
Besides, advertisers mostly care about demographics, since that's how companies define their target markets. And most of this information can be gathered from just a few sources, so the type of advanced data analysis in your example is not even required in practice. Whether someone is at risk of having a car accident would be more valuable to insurance companies, than for advertisers to decide what product to show them.