A/B testing your SEO changes can bring you a competitive edge and dodge the bullet of negative changes that could lower your traffic. In this episode of Whiteboard Friday, Emily Potter shares not only why A/B testing your changes is important, but how to develop a hypothesis, what goes into collecting and analyzing the data, and thoughts around drawing your conclusions.
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Video Transcription
Howdy, Moz fans. I'm Emily Potter, and I work at Distilled[1] over in our London office. Today I'm going to talk to you about hypothesis testing in SEO and statistical significance.
At Distilled, we use a platform called ODN, which is the Distilled Optimization Delivery Network[2], to do SEO A/B testing. Now, in that, we use hypothesis testing. You may not be able to deploy ODN, but I still think today that you can learn something valuable from what I'm talking about.
Hypothesis testing
The four main steps of hypothesis testing
So when we're using hypothesis testing, we use four main steps:
- First, we formulate a hypothesis.
- Then we collect data on that hypothesis.
- We analyze the data, and then...
- We draw some conclusions from that at the end.
The most important part of A/B testing is having a strong hypothesis. So up here, I've talked about how to formulate a strong SEO hypothesis.
1. Forming your hypothesis
Three mechanisms to help formulate a hypothesis
Now we need to remember that with SEO we are trying to look to impact three things to increase organic traffic.
- We're either trying to improve organic click-through rates. So that's any change you make that makes yours appearance in the SERPs seem