We set audience criteria where the user account must be created after the test launches, from there its a 50/50 split control/treatment experience (based on user id). The metric we are optimizing for is almost always conversion rate. We will turn the experiment off early if the treatment group is having really poor numbers, otherwise once about 4000 accounts have been entered into the experiment we plug the numbers into a bayesian calculator, and call it a winner of there is a 90%+ probability that the treatment beats the control. https://www.abtestguide.com/bayesian/