In principle, more analytic power can be achieved by varying multiple things at once in an uncorrelated (random) way, and doing standard analysis, such as multiple linear regression. In practice, though, A/B testing is widely used, because A/B tests are easy to deploy, easy to understand, and easy to explain to management.

With limited training data, a more constrained model tends to perform better.