If nothing else, they have a hypothesis to test in the next experiment.
Even when possible to isolate and remove ancillary changes to improve split test purity, it's often not beneficial. If there's a significant number of changes, achieving statistical significance across the full matrix probably isn't even possible.
But that's ok, because limiting changes to a single test queue restricts your ability to move fast and try lots of stuff, which is beneficial. So when you test, try cheap multivariate methods (there's a bunch!) to quickly understand how interactions between multiple changes affect results.
Even when possible to isolate and remove ancillary changes to improve split test purity, it's often not beneficial. If there's a significant number of changes, achieving statistical significance across the full matrix probably isn't even possible.
But that's ok, because limiting changes to a single test queue restricts your ability to move fast and try lots of stuff, which is beneficial. So when you test, try cheap multivariate methods (there's a bunch!) to quickly understand how interactions between multiple changes affect results.