I think it depends on the context. Machine Learning and statistics isn't magic, and you definitely don't want to use it for the wrong reasons if you can figure

I think it depends on the context. Machine Learning and statistics isn't magic, and you definitely don't want to use it for the wrong reasons if you can figure it out with domain knowledge. I would just like to throw in here if you are going to be featurizing users to do churn prediction, I would highly recommend doing a whole data pipeline and do profit curves. A data driven approach is only as good as the algorithms used. If you used profit curves to augment churn prediction (churn prediction is a pre req), you can figure out how much a user is actually worth bringing back. This also allows you to figure out an optimal budget to profit ratio on a cost of a user. That being said, use it only if you have enough data to be accurate. And also only use it for the right reasons. Done routinely, I believe it can be a great investment.