The terms “A/B testing” and “multivariate testing” are often used interchangeably, but they are in fact different forms of testing and should be used for different purposes.Read More
Where should I put my call-to-action? What’s the right messaging for this campaign? What color works better for this button – blue or green?
If you’ve ever gotten lost in the minutia of a campaign, you’ve likely asked yourself similar questions. The truth is, you can’t really know what will work until you’ve tested it. So today, we’re talking about A/B testing.
(This is the third post in a series about Google Analytics Content Experiments. For more information about Content experiments in Google Analytics (GA), you can view our first post two posts: Announcing Google Analytics Content Experiments and How to Create a Content Experiment in Google Analytics. )
So you’ve run your first content experiment in GA, now what? Well to answer that, we need to take a step back.
Hopefully your experiment began when you identified either an opportunity to improve your conversion flow or perhaps reduce the bounce rate on a landing page. Either way, your next step should have been to develop a hypothesis of why the page was not effective. Perhaps there was not a clear call to action, so you added a large button to encourage visitors to convert. Whatever you decided, your steps should have been
A. Identify a testing opportunity
B. Develop a hypothesis as to why the page is not effective and how it can be improved
C. Create a new version of the page and launch your experiment to test this hypothesis
D. Let GA determine which page is more effective
At this point there are three possible results:
Believe it or not, your next step is the same regardless of which result you saw. Let’s explore this. If you’re original page performed better or the same as the new one; that does not replace the original observation that this page/step/process could or should perform better. If your new page performed better than the old one then good for you; but that does not mean that it can’t still be improved. So as you may have surmised by now, the next step is to develop another hypothesis and run another experiment.
The good news is that there are new features available in Content Experiments to allow you easily pause, restart and re-launch saved experiments.