A/B testing within SEO: My guide and strategy
In the world of SEO, standing still is going backwards and implementing the wrong strategy “cannot be reversed.” That’s why I embrace A/B testing as an essential part of my SEO strategy. These tests allow me to continuously improve my website and ensure that it performs optimally in search engines. By systematically testing different elements of my website, I gain deep insight into what works and what doesn’t.
What are A/B tests (within SEO)?
A/B testing is an essential tool for me in optimizing my SEO strategies. The idea is simple: I create two versions of a web page (A and B) and test them among a similar target audience. By comparing the performance of these two versions, I discover which elements work best for SEO and user experience.
This need not apply only to pages that are duplicated, by the way. This could be about page titles you test, meta descriptions, headers, images, etc. As long as you create an environment where you can safely test options.
The importance of A/B testing in SEO
As a CMO or marketing manager, you know that small changes can have a big impact on SEO performance. A/B testing allows you to systematically test and implement hypothesis-driven changes. This not only improves search engine rankings, but also improves a website’s user experience and conversion rates.
For me, the biggest reason I like using A/B testing within SEO is to avoid losing certain positions. Before making a change on a website, you are never 100% sure if it will be a positive change for Google’s rankings. This could perhaps be done with a position 10-100, but you cannot take such a risk when you are already in the top three.
Are some adaptations “safer” than others?
That absolutely. There are some modifications that pose a higher risk to Google positions than others. Basically, think of everything you modify about the content (on-page SEO, images, videos and text) on the page itself.
So imagine, you’re in the top three for a particular search term in Google. In that case, it is very risky to make an adjustment in, for example, the page title or meta description of this page. This is because you can never know 100% prior to this adjustment whether it will result in a higher or lower position. Beyond that, Google will revisit the page and thus re-evaluate it, which carries additional risk.
Definition of A/B testing
At its core, A/B testing is an experimental approach in which two versions of a Web page are tested against each other. The goal is to determine which version has better performance based on predetermined KPIs, such as click frequency, time on page, or conversion rates. This method gives immediate feedback on what works and what doesn’t.
How A/B testing works
I start by choosing a web page and a specific element to test, such as the header, call-to-action, or layout. I then create an alternate version of this page (the “B” version) with the intended change. Both pages are then shown to a portion of my audience. Using tools such as Google Analytics, I analyze the performance of both pages based on preset goals.
This seems complex, but is made fairly simple by programs such as Optimizely and formerly Google Optimize (as of September ’23, it is no longer used).
Difference between A/B testing and multivariate testing
While A/B testing focuses on testing one variable at a time, multivariate testing allows me to test multiple elements and their combinations simultaneously. This can be particularly useful when interactions between different page elements are of interest. However, these tests require more traffic to generate statistically significant results and are more complex in analysis and execution.
Improving user experience
I always focus on improving the user experience because it has a direct impact on SEO performance. A good user experience keeps visitors on my site longer and lowers the bounce rate. I use A/B testing to optimize various aspects of the user experience, such as navigation, page load speed and content readability. By experimenting with these elements, I find the best combinations that attract and retain visitors.
So when I say that I A/B test for SEO purposes, it may well be that I am secretly focusing more on the user experience, but in turn indirectly positive for a website’s SEO. All user metrics can be important to the final ranking in Google. It’s all about offering as much value to the visitor as possible from Google and that Google understands this from these user metrics.
Conversion rates are crucial to the success of a Web site. A/B testing helps determine which elements on certain pages lead to the highest conversion rates. This can range from the color and placement of a call-to-action button to the effectiveness of product descriptions and images. By constantly testing and optimizing, I ensure that every page on my site contributes to my ultimate conversion goals.
Again, you can differentiate between the types of conversions you want to generate from your website. Per website, I would always recommend setting up at least 2-3 different types of conversions that immediately cover the entire customer journey. Examples include downloading a white paper, filling out a contact form or making a call.
Impact on search engine rankings
I am aware that user experience and conversion rates also affect search engine rankings. Search engines such as Google prioritize sites that offer a positive user experience. Therefore, not only do my A/B tests provide immediate improvements in user interaction and conversions, but they also help my pages rank higher in search results.
Before I begin A/B testing, I set clear and measurable goals. This can range from increasing the time visitors spend on a page to increasing newsletter subscriptions. By setting specific goals, I can more effectively evaluate the results of my tests and ensure that my efforts contribute to my overall marketing strategy.
Important in determining these goals is how they relate to the organization’s OKRs. I wrote a separate article about this, you can find it here.
Based on my goals, I formulate hypotheses about what I think will improve my site’s performance. For example, if my goal is to increase conversion rates, my hypothesis may be that a larger and more prominent call-to-action button will lead to more clicks. These hypotheses are the basis of my A/B testing and help me focus on specific changes I want to test.
This way, you can create a longlist and a shortlist of hypotheses that might contribute to the website’s conversion/SEO. From this shortlist you test the most interesting options.
Preceding this process is an inventory of the hypotheses you want to test. Possible ways I use for this are.
- Target group research (qualitative)
- Target group research (quantitative)
- Brainstorm with certain departments (often sales/product/marketing)
- Do site research (run tests on the site with your target audience)
Selecting test elements
The next step is to select the elements I want to test. These can include things like title tags, meta descriptions, and content structure. I choose elements that are likely to have the greatest impact on my goals and hypotheses. By systematically testing different elements, I can discover which changes have the greatest positive impact on my site. The process is thus.
Determine objectives –> Formulate hypotheses –> Determine tests that support this –> Testing.
Implementing successful changes
Once I determine through A/B testing which changes contribute positively, I ensure rapid implementation. These optimizations range from minor changes in content to larger changes in site design or navigation. I also ensure that these changes are implemented consistently across all relevant pages and platforms to improve uniformity and SEO performance.
Learning from test results
Every A/B test gives me valuable insights, regardless of whether the results are concrete or not. I learn not only what changes work, but also more about the behavior and preferences of my target audience. These insights are crucial for future marketing strategies and help me make more informed decisions in my SEO approach.
From this strategy, it is possible to create, for example, a list of hypotheses + score. This can be done in a simple spreadsheet that allows you to monitor the overview so you don’t run duplicate tests or test the wrong things. Future hypotheses must also conform to the successful hypotheses and not contradict them.
Continuous optimization and repeated testing
SEO is an ongoing process, which is why I see A/B testing as an ongoing activity. After implementing successful changes, I begin new tests again to identify further improvements. This cycle of testing, learning, and optimizing helps me continually refine my website and ensure that it remains competitive in the ever-changing world of search engine optimization.
This never actually ends. Especially for the somewhat larger B2B websites where each hypothesis takes longer to test or the larger web shops where you simply have a lot of things to test. Keep doing this as long as it benefits you.
A/B testing is a powerful tool in every SEO professional’s toolkit. The lessons I learn from each experiment allow me to continually improve and adapt my website to the changing needs of my target audience and search engine dynamics. This approach ensures that my website is not only performing well today, but also ready for tomorrow’s challenges and opportunities.