This gives you a distribution unrelated to active use, puts users in the same bucket (with the same number you’re going to have the same users in the first 10%) and links combinations together.

Often problems are more complex than they seem at first sight and I have found it’s a good approach to think “what am I missing” rather than “lots of people must be making very obviously bad decisions” and reach the latter conclusion only after more work. Usually I’ve missed something.

This just tells me you haven’t worked on a big/complex enough system.

If it were that easy people would not be paying for it.

Assuming you want a random distribution and don't want to take any other attributes into account.

We're a small company but new feature release for big features is typically targeted at low risk users/customers first. That usually means a few attributes are taken into account (age, customer value, customer sentiment, which features they use)