SEO experiments do not function in the same way as “traditional” A/B tests for conversion pages or emails; they require a different framework. Before discussing the A/B split test, let’s do a little bit of background work to understand the differences between A/B tests and what we typically see in digital marketing, known as CRO or conversion rate optimization.

What is A/B Split test?

What-is-AB-Split-testing?

The short version: A/B testing helps you figure out what Google wants from your site.

The more extended version: A/B split testing allows you to systematically test “control” against “variant” groups of pages to yield several benefits, including:

  • Cost-justifying SEO and marketing activities based on data (not hunches).
  • Preventing changes to your site from having unforeseen negative impacts on SEO.
  • Website ranking better in the right types of searches.

What is the difference between the A/B split tests and classic A/B tests? 

What-is-the-difference-between-SEO-experiments-and-classic-AB-tests

For a conversion rate split test (or classic A/B test), visitors are randomly sent to either version A or version B of a web page where the different call to actions can be tried out and various product images and other features tested.

To test a conversion page, you need three things:

  •   Users that are randomly divided into two test groups,
  •   2 versions of the webpage
  •   and a parameter (in this case, leads) to ascertain which page is “best.” 

However, this method cannot be directly applied for an A/B split test. If you were to place several different versions of a web page online to ascertain which gets more traffic, Google would immediately set off an alarm. The pages would destroy each other, and eventually, Google would not index one of them, so that the split test would not produce any worthwhile result.

There is, however, a framework that you can use to test whether and how modifications can affect the ranking of a page.

Why does A/B Split Test?

Why-do-AB-Split-Testing

Performing tests to improve your ranking in search engines is something that requires large investments. This task takes a long time to carry out each experiment, formulate the hypothesis, prepare the site, collect the results, and analyze the data to find useful information.

If it generates so much work, why do A/B Split Testing? Why not opt ​​for a WordPress ready template? The answer is simple: testing gives benefits that no template can deliver.

Data-based decisions

Data-based-decisions

Managing a website and a Content Marketing Strategy involves making decisions often. There are hundreds of variables that must be evaluated and monitored, the consequences of which may include success or failure.

No decision should be made using instinct as the basis, as this generates unpredictable results. Although the results are positive initially, there is no guarantee that they will continue in the medium or long term.

Only data-based decisions guarantee reliable, positive and sustainable results – and for that, it is necessary to carry out tests and experiments. If you test your site’s elements regularly, you will gather a diversity of data to direct your actions in the best possible way.

Greater control of consequences

Greater-control-of-consequences

To explain one of the A/B Split Test’s advantages, let’s look at Pinterest’s story, that social network where we share images and videos of different topics.

Pinterest’s team of engineers had been working on on-site improvements for some time and, for several reasons, decided it was better to render the platform’s media files in JavaScript. The technical part of the matter is beside the point, but the most important thing is that they decided to do a little test before implementing the change throughout the site.

Initially, they thought Google would crawl Pinterest content with the new rendering system normal, with no old method differences. However, during testing on a limited number of pages, they realized substantial drop-in access.

The new rendering system did not communicate very well with the search engines, and the pages started to rank very poorly. Therefore, they chose to cancel the change plan and decided to study the subject further to understand what, in fact, happened.

For us, the most important thing is how this SEO test prevented a catastrophe on Pinterest. If the entire site had received the new system, visits would have plummeted, and the platform would have a lot of work (and cost) to reverse the scenario.

So it’s important to test often, especially before significant changes to your site. Thus, we can anticipate and control possible damage caused (and implement changes that we know to be positive with more security).

The greater organic flow of visitors

The-greater-organic-flow-of-visitors

If you find that implementing an H2 subtitle in an article on your blog can increase that page’s placement by 10% on search engines, what would you do? Logically, it would implement the feature on all pages. As a result, you would see all of your articles ranking better.

With this positioning, your blog’s number of visitors would increase, generating more fuel for your Content Marketing strategy. What if you found that some other specific change helped improve your articles’ positioning by 7% if, after that,? I would certainly implement this change and see the cycle repeat itself, increasing visitors’ organic flow. And so would you. Right? 

The basic idea is that A/B Split Testing helps understand what works and what doesn’t rank your site better. If we identify the best options, then the site can be optimized and receive more visitors.

Preparation against updates

Preparation-against-updates

One of the fears of those who have a Content Marketing Strategy is to see all their work losing reach with one of Google’s algorithm changes. In some cases, an update can bite a portion of your organic traffic – in others; it can increase your reach.

Optimization tests help to mitigate the effects of these updates. First, they allow us to identify what has been changed more easily. After that, with the knowledge of previous tests, we were able to formulate hypotheses that help guide the recovery (or optimization) of the effects caused by the algorithm change.

What is the logic behind the A/B split test?

What-is-the-logic-behind-this-technique.jpg

Now that you know what A/B Split Testing is and its benefits, you need to understand how it works. A/B Split Testing is a variation of the Experimental Methodology applied to website optimization. Experimental Methodology works by manipulating a variable within a set of rules to see the significant changes caused by this change.

To guarantee the experiment’s scientific rigour, we always maintain a control group that remains in the same environment without changing the same variable. A/B Split Testing is just that. We modify a variable on a page (or a set of pages within a website) and maintain another set of equal weight without the change as the control group.

Then, we compare the two results to understand if there has been a significant change that indicates good SEO practice. As we created a control group with other pages of the same strength, there is no risk that the study will be contaminated by seasonal changes, such as the poor competitor’s performance.

For example, suppose you created Group A and Group B for the SEO test. Group A is the modified one, and Group B is its site without changes. If Group A had a statistically significant increase, it is a sign that the change is vital to improve its ranking. If the traffic growth were something seasonal and unrelated to the change, it would have affected Group B as well, since both were in the air simultaneously, and the only thing that separates them is the object tested.

That’s why A/B Split Testing is a safe way to find out how to optimize your website’s ranking on Google.

What can you A/B split test?

What-Can-You-Test

Here are a few essential features of the website you can optimize by running A/B tests:

Metadata

Metadata

Test the meta tags of your website by dividing the URLs into two groups, including targeted keywords in the meta title and meta descriptions and generic ones.

Header & Title Tags 

Header-Title-Tags

Group web pages by header and title tags (H1, H2, H3) where one set has the primary keywords you are optimizing for and the other set does not contain those same keywords. Run A/B tests to decipher which pages perform better.

Internal Links 

Internal-Links

Create a group for the pages linked to other pages of your site and another group for those that aren’t. Check the heatmaps and user behaviour reports to learn about the performances of each group.

Product Descriptions

Product-Description

A/B test the descriptions and keywords of your products. Check Google Analytics to review your tests’ performance based on SERP visibility, traffic, bounce rate, and conversions.

Images 

 Images

You can test different images and alt-tags on specific pages of your website. Check how they impact your website’s load time, performance, clicks, and user behaviour to understand what works best.

Be Mindful Of 

Be-Mindful

Before you start testing, keep in mind the following points:

Isolate All Elements That You Want To Test 

Isolate-All-Elements-That-You-Want-To-Test

Avoid testing everything in a single A/B test. Isolate the elements per SEO test case to have a clear idea of what is and isn’t working.

Don’t Confuse The Google Bots.

Dont-Confuse-The-Google-Bots

Make sure to de-index the test pages or add canonical tags to the actual page to help the Google crawlers index the right page.

Stick To A Plan 

Stick-To-A-Plan

Build a strategy, do the homework, and determine the factors you wish to validate before running tests. It can lead to massive discrepancies if you start running A/B tests without a concrete plan.

Conclusion

Conclusion

A/B split tests can help you gain insights into the performance of your website pages. Use these tests to frequently optimize your site for higher search engine rankings, engagement, and conversions. Just be sure to devise a clear strategy before running these tests, as it will help you obtain more accurate results.