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Search engine optimization (SEO) is crucial for driving traffic and visibility to websites. However, optimizing a site for search engines is often more art than science. There are no definitive rules that guarantee improved search performance. Instead, SEO professionals rely on constant testing and refinement and that’s where SEO A/B testing comes in to help.
But what exactly does SEO A/B testing entail? Will Critchlow, CEO of the SEO tech company SearchPilot, explains the ins and outs of this emerging strategy on The Business Storytelling Show.
What is A/B Testing in SEO?
In a nutshell, A/B testing involves showing different versions of webpages to users to determine which performs better. The terminology comes from categorizing the different versions as “A” or “B”.
For example, an ecommerce site might test two homepage designs – A and B. Half of site visitors would see version A, while the other half views version B. By tracking user behavior and conversions for each, the company can identify which design works best.
However, this specific approach doesn’t translate perfectly to SEO. As Will points out, search engines like Google essentially represent a single user, making even splits impossible.
Instead, SEO A/B testing focuses on comparing groups of similar pages, known as “site sections”. This could include location pages, product categories, job listings, etc.
The testing process involves changing one group of pages based on a hypothesis of what might perform better in search. The other group is left unchanged as a control. Advanced analytics then detect if the changes created a statistically significant difference in rankings and visibility.
So in practice, SEO A/B testing is more accurately described as split testing. But it relies on the same principle of experimenting between variants to optimize search performance.
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How Do You Do SEO Split Testing?
Conducting SEO split tests requires a rigorous process to deliver meaningful results:
1. Identify a Site Section
Isolating a group of related pages lays the foundation for accurate testing. As Will explains, this section needs substantial search traffic – at least 1,000 organic visits per day – for changes to be detectable.
2. Form a Hypothesis
Next, develop ideas for how to improve the pages’ search performance. This could relate to on-page content, metadata, structure, or other elements. The hypothesis should focus on better targeting user intent.
3. Select a Sample for Variants
Split the site section into two groups for testing. The control keeps pages unchanged, while the experiment group receives optimizations. The allocation between groups is designed to create two sets of pages that are as statistically similar to one another as possible.
4. Run the Test
Implement the hypothesized changes on the experiment pages. The length of testing can vary, but 2-4 weeks is typical to smooth out fluctuations.
5. Analyze Performance
Finally, analyze how the altered pages perform against the control in terms of rankings, click-through rate, visibility in SERPs, and traffic. Tools like SearchPilot automate statistical analysis to quantify differences.
The experiment may reveal positive, neutral, or negative results. Either way, data-driven insights help guide future optimization efforts.
How Common is This Strategy?
Despite its logical appeal, search split testing is still a relatively new and uncommon tactic. According to Will, it requires mature SEO programs before becoming beneficial.
The differentiating factor is scale. Large sites with millions of pages and daily organic visitors are better positioned for meaningful tests. Smaller sites face challenges achieving statistical confidence from page experiments.
However, Will expects testing to keep growing in popularity across all levels of SEO. Google increasingly leverages machine learning models to understand queries and match relevant pages. With search algorithms becoming “impenetrable black boxes”, the best way to optimize is trying new ideas and measuring outcomes.
How Can I Get Started?
For most websites, jumping right into advanced testing is premature. Focus first on proven SEO fundamentals, such as technical optimizations, quality content, and page speed. Establish clear tracking so you understand baseline visibility and traffic.
From there, sites receiving upwards of 30,000 monthly organic visits can start simple experiments. This allows building knowledge before expanding into a comprehensive testing program.
The capability to manage large-scale SEO experiments also requires data science skills. If lacking in-house expertise, leveraging specialists from vendors like SearchPilot bridges resource gaps. Combined with testing software to remove manual analysis, this makes SEO A/B testing accessible for more companies.
In Summary
SEO A/B testing is still uncommon despite offering evidence-based optimization. By comparing groups of pages instead of individual URLs, sites can accurately evaluate the search impact of changes. This emerging tactic stands to become particularly valuable on high-traffic sites, revealing marginal gains at scale.
So while far from universally adopted, split testing might hold the key for major brands to stay ahead in an increasingly competitive SEO landscape. Experimentation minimizes guesswork and risk to help pages better match user intent and search engine priorities.