A/B Testing is the practical approach to test one thing against another.
The concern some SEO’s and marketers have is that A/B testing could backfire. Concerns and questions such as:
- What If Google perceives my tests as an attempt to spam?
- Is A/B testing even allowed by Google?
- I’ll need to work with duplicate content to do these tests, is that allowed?
- How can I keep my test pages out of the Google index?
What Does Google Think About A/B Testing?
Google has often stated that A/B and multivariate testing are genuine marketing activities that don’t deserve a penalty in any way.
In fact, Google Analytics itself is a pretty good tool for A/B and split testing!
- Use rel=”canonical”
- Use 302s for redirecting
- Experiment only as much and as long needed
A/B Testing And Duplicate Content
Many A/B tests involve a design element, leaving the content untouched. So now you have two pages with identical content. Problem?
Google has said many times that there is no duplicate content penalty. John Mueller says that the main effect is that Google picks one version to show. And that if you have a huge amount of duplicate content, crawling new content can be affected.
Google Analytics Experiments let you run tests like this, which means Google acknowledges the need.
Isn’t A/B Testing Cloaking?
When you show one thing to users and another thing to Googlebot, that’s cloaking.
Google doesn’t like to send searchers to cloaked pages because what the user will see, is not what Google actually recommended.
But when you follow Google’s best practices for testing and treat Googlebot just like you would any user, there’s nothing to worry about.
As John Mueller puts it:
“So from our point of view, you should treat GoogleBot like you would any other user. So if you are doing A/B testing then GoogleBot probably needs to fall into one of those A or B test, how many variations you are testing slots as well.”
Never-ending A/B Tests, And How To Avoid It
In the best practices post Google says to not run a single test unmodified for too long.
“With regards for the duration, we just recommend to kind of keep it reasonable. In the sense that you shouldn’t be doing an A/B test for an extended period of time where you can say well, it is not an A/B test anymore, it is essentially the stable state for the site. So that is something where we don’t have any fixed time but kind of need to look at it on your site.
Some sites do regular A/B testing, that they go A/B and that they test it for a month and then they test other variations and next variations and they continually test and that is fine too.”
Good A/B test tools can tell you when there is enough data. Check how your tool deals with statistical significance.
* Adapted lead image: Public Domain, pixabay.com via getstencil.com
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