When A/B testing is not recommended because of regulatory requirements or technical limitations to setting up a controlled experiment, we can still quickly implement a new feature and measure its effects in a data-driven way.
When A/B testing is not recommended because of regulatory requirements or technical limitations to setting up a controlled experiment, we can still quickly implement a new feature and measure its effects in a data-driven way.