We suggested one point: you can't set up multiple test elements on a classic A/B solution. But what about testing multiple variables? Should I test one point at a time?
not functional to the good work to be done. For this reason, multivariate tests must be used, that is? Specifically, we are talking about:
“ An experiment that tests two or more sections to taiwan mobile number example understand their effects on each other. For example, variations of a headline can be tested at the same time as variations of a main image. Instead of showing which variation of a page is more effective (as in an A/B test), a multivariate test identifies the combination of variations that is most effective.”
It seems like a good idea to focus on these solutions. In reality, multivariate tests can hide several problems. First of all, because they are difficult to set up optimally , they are sophisticated tools that require expertise and experience to be set up. Furthermore, they are designed to answer dozens of doubts, but they are not always able to delve into one aspect.
multivariate test
While an A/B test only answers one question in the best possible way, multivariate gives many answers, saving time. But that doesn’t mean the data generated is more useful. It also requires a lot of traffic. In short, multivariate testing is convenient and useful, but not always suitable.
Precautions for a good UX
As you can understand, thanks to Google Optimize you can set up specific tests, dedicated to precise aspects. All this without affecting performance and user experience. For example, thanks to the anti-flicker snippet that eliminates the annoying flickering effect in A/B and multivariate tests.