What is SEO A/B Testing?

SEO A/B testing refers to a type of test in which we put pages of a similar type into two groups. A control group and a test group.

The Control group forms the baseline for our SEO A/B tests. We do not change these pages during the testing process. We monitor their search performance to serve as a baseline for our test group of pages.

Speaking of test pages… These are the pages that we do, in fact, change during SEO A/B tests. Again, we monitor the performance of these pages and compare this to the performance of our control group pages. This allows us to see which “version” of the page performs better in terms of organic search performance.

That’s how we get results for SEO A/B tests.

How is that different from “traditional” CRO A/B testing? Well, rather than comparing two versions of the same page, we’re comparing two groups of similar pages in order to determine whether a change improves SEO performance.

The concept remains incredibly similar; we just do it in a slightly different way.

Table of Contents

• The Advantages of SEO A/B Testing
• What Can You Test with SEO A/B Tests?
• How to Set Up an SEO A/B Test
• Tools to Help You Run SEO A/B Tests
• Common Pitfalls and Mistakes to Avoid
• SEO A/B Testing Case Studies
• Frequently Asked Questions

The Advantages of SEO A/B Testing

So now we’re all aware of what SEO A/B testing actually is… What are some of the advantages that we’ll be able to see when we start running these types of tests regularly?

• Tests based on multiple pages
• More scientifically accurate results

One of the main advantages of conducting regular SEO A/B tests is that the results are often more reliable than choosing to use time-based SEO tests. Given that you are comparing results over multiple pages, with a control group set up, you can be more assured that the results you are seeing are down to causation and not correlation. In other words, you can be more certain that the results you are seeing are down to your changes, and not a random change that you could not control.

Note: The above is not to say that time-based SEO tests are bad! They still have their place in a well-rounded SEO testing program. But, in some situations, SEO A/B tests can give more scientifically reliable results!

SEO A/B tests are also the perfect SEO test type for large websites with lots of pages using the same style of template. Ecommerce websites are the perfect candidate here. You can run SEO A/B tests on product display pages, product listing pages, and any other page templates that are similar, such as blog pages, in order to determine where your SEO strategy goes in order to bring in better results.

What Can You Test with SEO A/B Tests?

The list of things you can test with SEO A/B tests is almost endless. From small changes to your page’s title tags and meta descriptions to full website template changes.

In this section, I want to give you some examples of different SEO A/B tests to give you more of an idea of how to start for your own website.

Title Tags and Meta Descriptions

Testing title tags and meta descriptions is one of the most common types of SEO A/B tests run on the SEOTesting platform. SEOs will often test different titles and/or meta description variations to see which performs better and generates more organic traffic from Google.

Take a look at this example from one of our customers:

Content Length and Format

Another test commonly run on the SEOTesting platform is experiments with content length and format. This is one thing that is within your absolute control when you write new content or refresh existing pieces.

This image here shows the results of an SEO A/B test where our client added an FAQ section and expanded the content this way:

Page Speed and Core Web Vitals

We know that page speed (and Core Web Vitals) plays a role in the position Google shows your website in the SERPs. We don’t know exactly what weight this has within Google’s ranking algorithms, but we’ve been able to show from many SEO tests that faster sites lead to better on-site metrics, which then lead to better rankings and more organic traffic.

Perhaps you’d like to spend some dev time to improve your page performance, make it faster, and try to reduce bounce rates? SEO A/B tests are perfect for showing the results of this kind of work!

Structured Data

Structured data has always been important, simply because it helps search engine bots understand your content and rank it for relevant queries. However, some people are now arguing that it is becoming more important because it enables LLMs to perform similar tasks.

Now this is all conjecture at the moment, but what’s the harm in running SEO A/B tests to see whether adding structured data increases SEO performance? Take a look at this example showing a performance improvement when product structured data was added to a website:

H1s and Subheadings

H1 tags and subheadings (H2 tags, H3 tags, etc) are great subjects for SEO A/B tests. If you’re an SEO specialist and you want to get more traffic from your existing content, changing your H1s and subheadings is a great way to do that. And you can see some pretty incredible results from doing so!

Take a look at this example where an SEO team removed published dates from H1 tags:

Product Listing Pages

At the start of the article, you might have seen me mention that product listing pages are prime examples for SEO A/B tests. I wasn’t lying!

Given that these pages are often the page type on ecommerce websites with the highest ranking potential (think queries like running shoes, golf clubs, car parts, etc), it makes sense that running SEO A/B tests on these can have some incredible results both in terms of organic traffic AND revenue generated from organic traffic.

Product Display Pages

Much like product listing pages, product display pages also make great candidates for SEO A/B tests. It’s these pages that are the final stop before a user purchases, so any improvement you can make (to both SEO performance and conversion rates) can have a direct impact on business performance.

Especially when you use a tool like SEOTesting, which allows you to run SEO A/B tests whilst measuring Google Search Console data and Google Analytics events. You can see the SEO impact of CRO changes and the CRO impact of SEO changes.

How to Set Up an SEO A/B Test

In this section, I’ll guide you through the process of setting up your first SEO A/B test.

As someone who’s run many of these from both during and before my time at SEOTesting, I can tell you this process is incredibly simple, even though it seems complicated off the bat.

Formulate Your Hypothesis

The first step is to formulate your hypothesis. In other words, make a prediction as to what you think will happen once you have made the change to your test pages.

Your hypothesis will determine everything that happens during your SEO A/B test. It will determine what metrics you track, the changes you will make to your test pages, and even the timeframe for which you will run your SEO A/B test.

So yes, your hypothesis is crucial.

If you want some great advice on how to create a hypothesis for your SEO A/B test, I’d recommend watching Giulia Panozzo’s SEO Testing Workshop, which she did in conjunction with Sitebulb.

Define Your Control and Test Groups

Once you have formulated your hypothesis, it’s time to define your test and control groups.

As I mentioned earlier, your test group is the group of pages that you will be changing, and your control group is the group of pages you will be leaving unchanged and measuring performance against.

My advice? Find pages with similar traffic levels that share the same page template. So, for example, if you’re running an SEO A/B test on an ecommerce site, you could choose to use your product display pages. Or if you were running a test and changing your blog template, you should, of course, use your blog pages for this.

If you need assistance determining your test and control groups, you can make use of SEOTesting’s A/B Test Group Configuration Tool.

Implement the Change to your Test Pages

The next step is to implement the change to all of your test pages.

No matter the change you are making to your site, ensure that all tasks are completed as one job. Avoid changing your test pages over a few days, as this can disrupt the data you track and affect the results you see.

So, whether you’re making the change to a group of ten pages or a hundred pages, make sure this is all done at the same time. The good news is that most CMS nowadays make this a relatively simple and quick process.

Measure Performance Over Time

Once your changes have been deployed to the test group of pages, it is time to start collecting data as the days roll on.

This can be done manually, from Google Search Console, or you can use a tool to collect this data automatically for you. As SEO A/B tests can be made up of tens or hundreds of pages, manually recording this data would be a huge time-sink.

We take a look at the tools you can use for automating the collection of your SEO A/B test data and presenting the results, but obviously, our recommendation is that SEOTesting is the first thing you should check out 🙂

Analyze the Outcome

It’s now time to analyze the outcome of your SEO A/B test.

If the test group of pages has outperformed the control group, you are able to say the changes you made to the test pages have had a positive effect on organic SEO performance. You can then decide on the next step (repeat or rollout), which we cover in the next section.

If the control group has outperformed the test pages, the changes you made to the test group are a negative from an SEO perspective, and you would need to make the decision whether to roll them back or further iterate.

Repeat, Roll Out Winning Variant, or Rollback

Finally, it’s time to use your data analysis to determine whether to repeat the test, roll out the change to the rest of your website, or rollback.

If the change you’ve tested is going to be rolled out to tens of thousands of pages, it is quite often a sensible approach to ensure a test result is repeatable. This is achieved by creating a second test and control group of pages, effectively re-running the test to ensure its repeatability.

You may also decide to re-run a test on new pages if the initial results are inconclusive.

Once you are happy with the results, either from a single run or a repeated test, you can make the decision to roll out the change to all the other pages on the site.

If your SEO A/B test led to a negative result, then you will, of course, want to revert your changes back to the original version.

Tools to Help You Run SEO A/B Tests

There are a number of online tools that you can use to help you run SEO A/B tests, with your three main examples being:

• SEOTesting
• seoClarity
• SearchPilot
• Split Signal from Semrush

In this section, I’ll give you a brief overview of each. So if you are looking for an SEO testing tool to help you with your A/B tests, you can find the best tool for you!

SEOTesting

SEOTesting is our own tool that we’ve built to help SEOs run SEO A/B tests so they can find what works and double down on that.

SEOTesting uses Google Search Console data as well as Google Analytics data, so you can get a broad overview of what happens during all of the SEO A/B tests that you run on the platform!

Simply create your SEO A/B test within SEOTesting, and the tool will automatically gather all of the relevant data for you. It’ll send you an email once the test has been completed, and you can jump into the tool to analyze results.

SEOTesting can also do statistical significance calculations for you during SEO A/B tests. To do this, you’ll need to set up group tests for your control and test groups (you can do this with the click of a button during the SEO A/B test setup) and then analyze the statistical significance of each group test once the A/B test has completed.

seoClarity

seoClarity’s SEO Split Tester forms part of its ClarityAutomate platform, aimed at helping enterprise SEO teams run SEO A/B tests at scale. The tool has been designed to minimize the need for extensive developer input or data science involvement, enabling teams to set up, deploy changes, and measure results within a single platform.

seoClarity’s testing tool is designed for large organizations already using the platform, with pricing available upon request.

SearchPilot

SearchPilot is an enterprise SEO A/B testing platform that’s designed to let large teams test changes at scale without needing to rely heavily on developer resources. The platform works by splitting groups of pages into control and test sets, applying changes to the test group, and measuring the impact on organic search performance.

Split Signal from Semrush

Split Signal is an SEO A/B testing tool developed by Semrush that is available inside of their Enterprise platform.

Common Pitfalls and Mistakes to Avoid

Look, SEO A/B testing is incredibly powerful, but there are a few common mistakes that can trip you up. Sometimes, these mistakes can lead to misleading or just plain wrong conclusions.

If you want to get the most reliable results from your tests, keep an eye out for these pitfalls.

Not Using Balanced Control and Test Groups

If your control and test groups aren’t similar enough (in terms of the traffic coming to those pages pre-test), then you risk introducing bias into your results.

For example, if your test group contains pages that already get lots more traffic than the pages in your control group, this is going to create an imbalance in the daily average that you’ll use to measure the results of your test.

Pre-changes, it’s really important that you ensure your control and test pages all have similar traffic numbers.

If you need help with this? Give SEOTesting’s A/B Test Group Configuration tool a try!

Not Testing on Similar Pages

Running SEO A/B tests on a mix of completely different page types (for example, blog posts, product pages, and landing pages all in one group) will muddy your results. You need to keep your groups to one specific template or page type, otherwise you are comparing apples to oranges.

Running Tests During Google Core Updates

As you are testing on pages that are all of the same type, algorithm updates should in theory affect them all in the same way. Any uplift or drop to the test group compared to the control group is down to the changes made. This is one of the benefits of SEO A/B testing: being able to test through seasonality and algorithm updates.

However, algorithm updates can be unpredictable, and while test results may be reliable during an algorithm update period, it is best to verify if an SEO A/B test is repeatable after the initial test concludes.

Ignoring External Variables

It’s not just algorithm updates you need to be looking out for during your SEO A/B tests. External factors, such as digital PR campaigns, seasonal demand changes, or even sitewide technical fixes, can skew your data in a positive or negative way. Always document what’s happening in your wider marketing activity and industry while your test is running so you can account for these variables in your analysis.

Drawing Conclusions Too Early

It’s tempting to look at early data and make a call, especially if the change seems to be working right away. But SEO takes time, and fluctuations in performance are completely normal. Ending a test too early can give you false positives or false negatives. As a general rule, let your test run for the full planned duration and aim for statistical significance before making decisions.

The only caveat to this rule would be if your traffic starts to tank on test pages during one of your SEO A/B tests. If this is the case, then you can choose to end a test early in order to “save” as much of the performance as possible. Although the better option is always to finish the test and then revert the test pages to their pre-change state.

Misinterpreting Correlation and Causation

Just because your test pages improved (or declined) doesn’t mean your change caused it. Sometimes, other factors like competitor actions, new SERP features, or broader search trends can drive performance shifts. Always use your control group comparison to help distinguish between correlation and causation. Repeating a test will always increase confidence in the results.

SEO A/B Testing Case Studies

To show you just how powerful SEO A/B testing can be, let’s look at a few real-world examples from tests run using SEOTesting.

A quick note on how we measure SEO A/B test results at SEOTesting:

For SEO A/B tests, we calculate a daily difference in clicks between the test group and control group of pages (the green line).

That daily difference is then averaged out before the changes were made to the test group, and after the changes were made. These are represented by the blue and black lines on the charts shown in the case studies.

If the black line appears higher than the blue line, we can say that the test group of pages has outperformed the control group, since the changes that we wanted to test we made to the test group.

Testing a Page Redesign

SEO A/B tests are great candidates for testing page redesigns, and that’s exactly what was tested here for this voucher code website.

They wanted to test a simplified version of a page, making the information more readily available, removing fluff, and designing the page so it looked “nicer” for the users who landed on the site.

As you can see, this SEO A/B test led to a positive result, with a 94.55% increase in the average click difference. You can also see on the scorecard that the test pages improved their performance (via a clicks per day measurement) by over 200% while the control pages actually declined in performance.

Redesigning a Category Page

A car comparison site had the goal of improving the usability and general visual appeal of its category pages across its site.

They ran an SEO A/B test, comparing the old design (the control pages) to the new design (the test pages). Following the test, you could see a 33% increase in click difference. This showed that improving the page’s design has a measurable AND positive impact on organic traffic.

Adding Year and Month to Page Titles

A price comparison site tested adding the current year and month to the titles of key landing pages. The goal for this test was to increase page relevance, improve the click-through rate from the SERPs, and show freshness to both users and search engines.

The test ended up leading to a massive 1268% increase in click difference. That’s a clear example of how a relatively small change (in the grand scheme of things) can lead to massive gains in a short period of time.

Frequently Asked Questions

To wrap up, let’s answer some of the web’s most common questions about SEO A/B tests.

What is SEO A/B testing?

SEO A/B testing refers to the process of conducting an SEO test using a control group (where pages are unchanged) and measuring the performance of a test group (where the pages have been changed). You then analyze the results of both groups to see which performs better.

What’s the difference between CRO A/B testing and SEO A/B testing?

While the principle is the same for SEO A/B testing and CRO A/B testing (putting two groups/versions against each other), there is one significant difference. In CRO A/B testing, you can compare two versions of the same page/feature. We can’t do this in SEO. SEO A/B testing refers to the process of comparing the performance of different but similar types of pages in a control group and test group.

What tools can help with SEO A/B testing?

You can use free tools to help you with SEO A/B testing, including Google Search Console and Google Analytics. But if you plan on running SEO A/B tests regularly, I’d suggest using a specialist SEO testing tool like SEOTesting, seoClarity, SearchPilot, or Split Signal from Semrush.

What can you test during SEO A/B tests?

The list of things you can test during an SEO A/B test is almost endless. You can test anything from small website tweaks, like your page titles and meta descriptions, for example. Alternatively, you can test larger changes, such as site performance improvements or new page templates. You could even conduct an SEO A/B test on a new website design!

How long should you run an SEO A/B test?

This is extremely subjective. But as a general rule of thumb, you will want to ensure you are running SEO A/B tests for six to eight weeks in order to give you the best chance of reaching a statistically significant result. However, sites with much higher traffic volumes will achieve significance quickly, and therefore, you can run tests for shorter periods of time.

SEO A/B testing isn’t just a “nice to have” for your website. It’s actually one of the most reliable ways for you to understand what’s actually moving the needle in a positive direction and working for your site! By running SEO A/B tests in a structured, careful way, and avoiding all those common pitfalls, you can make confident, data-driven decisions that drive real growth!

Whether you’re tweaking title tags, reworking page templates, or testing entirely new layouts, the key is to test, measure, and learn! Over time, these small, validated improvements can compound into massive gains for your organic traffic, conversions, and revenue.

If you are looking for a specialist SEO testing tool to help you set up and run SEO A/B tests easily, check out SEOTesting. We have a 14-day free trial, with no credit card required on sign-up. Visit our website for more information, or sign up for your trial today.