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.
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)
you pick a frequency you want, represent that as a fraction, and modulo on user id, and your 80% of the way there
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.
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.
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)