SEO testing, as you probably know by now, is the systematic process of making changes to your website and measuring their impact on search performance.
When we think about it, we’ve been doing SEO testing since SEO became a practice. We’d find something we weren’t happy with (traffic, rankings, etc), and we’d change something on our site and measure the results. But with the release of new tools, we can take SEO testing to a whole new level.
While traditional SEO relies heavily on adhering to best practices and making educated guesses, proper testing allows us to validate our assumptions with data-driven evidence.
The importance of testing can’t be overstated. Google makes thousands of algorithm updates every year, and core updates every few months dramatically reshape the SERP. What worked six months ago might be completely ineffective today. Additionally, user behavior shifts constantly, and competitor strategies evolve quickly, too. Without testing, we’re essentially flying blind, relying on assumptions rather than evidence.
In this article, I want to discuss some advanced SEO testing methods we can use today, how to measure statistical significance to ensure we have enough data for our tests, and share some real-world case studies.
Advanced SEO Testing Methods
To start, let’s review some of the SEO testing methods available today. These include pre/post-testing, split-testing, and multivariate testing.
Pre/Post Testing
Pre/post testing is probably the most common form of SEO testing currently available in our toolset.
This type of testing involves tracking the performance of one page (or a group of pages) over a period of time, then making a change to this page and tracking the performance of the page after the change has been made.
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This type of testing will work well for small updates, such as internal link additions, content refreshes, adding Schema, etc.
Split Testing & A/B Testing
There is a common misconception about split testing and a/b testing, especially from an SEO perspective.
A/B testing refers to the practice of creating two versions of the same page and then running both versions simultaneously to see which version performs better.
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This is great for CRO testing! You can use this test type to find out what call to action works better, for example. But when it comes to SEO testing, we know that sending users to two versions of the same page can cause a host of issues.
This is where split testing comes in.
Split testing refers to the process of creating two groups of the same page types (think ecommerce PDPs or blog posts in the same content cluster), changing something within one of the groups (your test variant), and then testing performance against the control group of pages.
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This allows you to assess whether your changes have worked without risking Google penalties for duplicate content or cloaking.
Multivariate Testing
Multivariate testing is the process of testing multiple changes on your website simultaneously. Rather than testing a single change, like a meta description change, you would change multiple elements in one test and then measure performance over time to see what happens.
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There is a downside to this test type, however.
If you test multiple element changes simultaneously, you may not be able to see which change worked for the site in particular.
When it comes to Google’s algorithm, we know it’s a black box. Our endeavour with SEO testing is to find what changes on a website open up that box, so testing multiple changes at the same time might not be the best idea.
Technical Implementation of SEO Tests
I now want to spend some time talking about how to actually set up SEO tests that will run smoothly on your website. This boils down to the following steps:
Selecting pages for testing
A note on creating test and control groups (for split tests)
Implementation methods
Selecting Appropriate Pages for Testing
The first step of setting up any successful SEO test is selecting the right pages to test. This will usually come down to one of the following:
A page that has been stagnant for a while.
A page that is underperforming.
A page that is listed high on Page 2 of Google, a quick win.
A page that has had a Digital PR campaign performed on it.
A page that has been impacted by one of Google’s core updates.
But, of course, the right pages for testing will depend on you as a business. For example, an ecommerce website might want to test one of their product category pages and try to get this ranking for more queries. A SaaS business might be trying to improve user engagement across their feature pages.
That’s the beauty of SEO testing. For any issue you have on your website, there is generally a test that can be performed to see if you can rectify it.
Creating Test and Control Groups (For Split Testing)
If you are running split tests on your website, you will need to set up both a control group and a test group to run your split test effectively. When setting these groups up, make sure you are following these key rules:
Ensure the pages in both groups have similar traffic levels.
Ensure they are the same page type (PDPs, PLPs, blog posts, feature pages, etc)
Ensure there are no ‘outliers’ within the group, such as pages getting a much larger share of clicks.
There are online tools for this. SEOTesting, for example, has a group configurator within the tool that will help you create both the test and control groups for your tests. Searchpilot handles all of the tests for clients via the software, meaning the software will pick the correct groups for you.
Of course, you can also do this manually, looking at data from software such as Google Search Console or by looking at your rankings through Advanced Web Ranking. The only downside to this is that it takes a lot more time, so it is harder to be agile with your testing when doing it manually. This, however, might not be an issue if you are not working on an extremely large website.
Implementation Methods
The implementation method you will use for your SEO tests will depend on whether or not you are using software, what software you choose to use, and your website’s CMS.
If, for example, you are choosing to do your SEO testing manually, your process will look something like this:
Form your hypothesis
Select the page (or pages) on your website that will be measured
Collect data for 2-8 weeks pre-test
Make the change on your page or pages
Collect data for 2-8 weeks post-test.
The above example uses a pre/post-test. Be aware that the approach will work slightly differently if you are doing split testing.
If you are using SEO testing software such as SEOTesting or Searchpilot, then your process might look like this:
Form your hypothesis
Select the page or pages on your website that will be measured
Make the change on your website
Set up the test in your SEO testing tool
Wait for the test to be completed
Using SEO testing tools is certainly a great way to save time when running SEO tests, but you can still run this process completely manually if you prefer.
Measuring Statistical Significance
One key factor we have yet to discuss is statistical significance. I want to take this opportunity to discuss that in detail, as this is where things can certainly get a little complicated if you are unsure.
Let’s first talk about the two main approaches to measure statistical significance, and then I’ll talk about what outside factors can impact your chances of running a statistically significant test.
Frequentist vs. Bayesian Approaches
The Frequentist approach defines probability as the long-run frequency of events over repeated trials. It uses p-values, confidence intervals, and hypothesis testing to measure statistical significance. It draws conclusions based solely on observed data without incorporating prior beliefs.
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The Bayesian approach, by contrast, treats probability as a degree of belief that can be updated as new evidence emerges. It combines prior knowledge with observed data to form posterior probabilities.
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Both methods have merits, but I believe the frequentist approach offers a few distinct advantages for SEO testing.
It provides objectivity that doesn't require subjective prior beliefs, making the results far easier to communicate to stakeholders.
Most SEO testing platforms are built around frequentist concepts, making implementation straightforward. The clear decision threshold (for example, p < 0.05) helps SEOs make consistent choices about implementing changes.
Additionally, frequentist methods tend to provide more conservative estimates, helping prevent false positives that might harm site performance.
For high-traffic websites with large sample sizes, the computational simplicity of a frequentist method becomes particularly valuable compared to Bayesian approaches.
Handling Algorithm Volatility and Noise
SEO test results are inherently noisy due to:
Algorithm fluctuations
Seasonal variations
Competitor actions
SERP feature changes
Crawl inconsistencies
To mitigate these challenges:
Use longer test durations to smooth volatility
Implement control groups that remain unchanged
Monitor industry-wide SERP changes during testing
Consider using causal impact analysis techniques
Test during periods of relative algorithm stability
Scaling SEO Testing for Enterprise Sites
Running SEO tests on enterprise websites requires a different approach than for smaller sites. The scale, complexity, and potential impact of changes demand rigorous methodology and cross-functional coordination to ensure successful outcomes.
Challenges of Enterprise-Scale Testing
Here are some of the biggest challenges you’ll face when you want to start testing on your enterprise website.
Complex Infrastructure
Enterprise sites will often have a technical infrastructure that includes multiple CMS, development environments, CDNs, and even specialized platforms that may respond differently to SEO changes.
This complexity can make it difficult to implement consistent tests across the entire ecosystem.
Release Cycles
This is probably one of the most common issues we see at SEOTesting. Enterprise sites usually run on release cycles that may not align with your testing timelines, and this can cause delays or force you to make conclusions when the test hasn’t fully completed.
Enterprise development calendars are typically planned months in advance, making it challenging to coordinate SEO tests with developer resources.
Difficulty Isolating Variables
When working in enterprise developments, it can be hard to isolate the variables that are working (and not working), and this challenge is often encountered. When multiple teams make concurrent changes to your website, determining what change had what impact becomes a little problematic.
Higher Stakes and Revenue Impact
Enterprise websites often bring in much more traffic (and, more importantly, revenue) than a smaller site might. This can make getting buy-in for tests an extremely difficult task because your stakeholders are worried that a test will lead to a downturn in traffic and revenue.
Loss aversion in SEO testing is real. I wrote about it on LinkedIn here if you are interested in learning more about how it impacts your SEO tests.
Multiple Teams
Conducting SEO tests in enterprise environments can sometimes be made more difficult purely due to the number of teams working on the site. If a change is made by one team (like UX, for example), this could potentially impact a test that the SEO team is running. This works the opposite way around, too. The SEO team might start an SEO test that then changes something the UX team was working on.
Strategies for Effective Enterprise Testing
However, there is a workaround for every challenge. In this section, I want to discuss some of the things we at SEOTesting have seen work in enterprise testing.
Communication
One of the best ways to overcome some of the issues you may encounter with enterprise SEO testing is to develop a culture of communication around testing.
Start with smaller tests, communicate to external teams that these tests are happening, and be sure to share the positive impacts successful SEO tests have. This will build a culture of buy-in toward SEO testing, making it easier to get team agreement when it comes to creating and executing SEO tests in the future.
Single Variant Testing
When possible, ensure you are only testing one thing on your website at a time. This can solve the common problem of not being able to identify the one variable that really moved the needle for your website.
Yes, this approach takes much more time, but to create a testing culture, you could describe it as a ‘necessary evil’ in that it is something you have to do to get more buy-in from your team for future tests.
Testing on Page Subsets
When you are afraid of rolling out changes that may negatively impact traffic and, therefore, revenue, it is always worth rolling out the test to a smaller subset of your less valuable pages first.
This allows you to test the impact, and you can be a little more confident that rolling out the changes to your wider pages will not negatively impact traffic.
Split testing is incredibly useful for this. You can roll out a change on a small group of your pages and test its impact before rolling it out across the rest of your site’s pages.
Testing Database
Building a database of completed tests is one task that will help you overcome the ‘loss aversion’ culture we see.
Using a database like this, you can go back in time to see when certain tests and experiments were rolled out and the impact they had. So when you come to do a similar type of job in the future, you can make an educated guess, which is data-backed, as to what the results could be.
Common Pitfalls
As with any SEO strategy, testing has its pitfalls. In this section, I will discuss some of these pitfalls and how to avoid them.
False Positives and Misleading Results
Several factors can lead to misleading test results:
Insufficient test duration
Inadequate sample size
Seasonal fluctuations mistaken for test effects
Confirmation bias in data interpretation
Ignoring external factors affecting performance
To mitigate these risks, implement robust statistical methodologies, establish clear success metrics before testing, and maintain strict documentation of test conditions and external events.
Google Updates During Experiments
Algorithm updates during test periods can completely invalidate results. When major updates occur mid-test:
Document the update timing and nature
Analyze whether the update targeted elements related to your test
Extend the test duration if necessary
Look for patterns in performance changes across test and control groups
Consider restarting the test after SERP volatility subsides
To determine causality versus correlation, examine whether performance changes align with your implementation timeline or with known algorithm updates. Cross-reference with industry-wide impact data from tools like Semrush Sensor or Mozcast.
Testing Beyond Rankings
Rankings alone provide an incomplete picture of SEO success. A comprehensive testing framework should include:
Click-through rate analysis
Conversion rate impact
Revenue and transaction metrics
User engagement signals (time on site, pages per session)
Return visit rates
Some tests may show no ranking improvement but still deliver significant business value through enhanced user experience metrics or conversion improvements.
Wrapping Things Up
Advanced SEO testing represents the evolution from opinion-based optimization to data-driven decision making. By implementing rigorous testing methodologies, SEO professionals can validate hypotheses, quantify impact, and build institutional knowledge about what truly works for their specific sites and audiences.
As search algorithms continue to evolve in complexity, the competitive advantage will increasingly shift to organizations with sophisticated testing frameworks that can quickly identify and capitalize on effective optimization strategies while avoiding resource investment in approaches that don't deliver results.
The most successful SEO programs will balance technical expertise with scientific testing methodologies, creating a continuous improvement cycle driven by empirical evidence rather than conventional wisdom or speculation.
Article by
Ryan Jones
Ryan is an experienced SEO professional with close to a decade of experience. In that time, he has worked for brands in-house, worked with agencies, and consulted on a freelance basis, too. So it is fair to say he has seen SEO from all angles. Over his near-decade-long career, Ryan has worked on hundreds of marketing campaigns for a range of companies, from small brands to multi-national corporations. Ryan is currently the Marketing Manager at SEOTesting, an SEO testing tool designed to help SEOs unlock Google's 'black box' and find the optimizations that work for their website.
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