To A/A test or not is a question that invites conflicting opinions. Enterprises, when faced with the decision to implement an A/B testing tool, do not have enough context on whether they should A/A test.
In this blog post, we explore why some organizations practice A/A testing and the things they need to keep in mind when running A/A tests. We also discuss how enterprises can decide whether or not to invest in an A/B testing tool.
A/A testing has become an essential tool for many marketers and businesses looking to optimize their websites and campaigns. But what exactly is A/A testing and how does it work?
In this comprehensive guide, we’ll break down everything you need to know about A/A testing, including:
- What is A/A testing?
- How A/A testing is different from A/B testing
- Why you should run A/A tests
- When to use A/A testing
- How to set up an A/A test
- Real-world examples and use cases
Whether you’re new to A/A testing or looking to better utilize it in your marketing efforts this guide will provide valuable insights, actionable tips, and best practices. Let’s get started!
What is A/A Testing?
A/A testing is a technique used to verify the accuracy and consistency of A/B testing tools and campaigns, It involves splitting traffic between two or more identical variants of a web page or campaign element and observing user behavior and metrics
The goal of an A/A test is to confirm that the A/B testing platform is working correctly by showing no statistically significant difference between the identical variants. If the tool detects a significant difference, it likely points to an issue with the testing setup.
Essentially, A/A testing serves as a control to ensure your A/B tests are providing true and reliable results before making any business decisions based on them. It helps validate that your testing tool is properly configured and integrated.
How A/A Testing Differs from A/B Testing
While A/A and A/B testing may sound similar there are some key differences
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Goal: A/B testing aims to determine which variant performs better. A/A testing checks for issues with the testing tool or setup.
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Variants: A/B tests have distinct variants. A/A tests have identical variants.
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Expected outcome: A/B testing expects to see a “winner.” A/A testing expects no significant difference between variants.
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Sample size: A/A tests often require a larger sample size than A/B tests.
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Test duration: Due to larger sample sizes, A/A tests take more time to complete than A/B tests.
Why You Should Run A/A Tests
Here are some of the key benefits and reasons to run periodic A/A tests:
Verify accuracy of your A/B testing tool
As mentioned earlier, the main purpose of A/A testing is to validate that your A/B testing tool is accurately splitting traffic and reporting metrics. If the A/A test shows a significant difference between identical variants, that indicates a potential issue with the tool that should be addressed.
Check analytics integration
By comparing your A/B testing tool’s reports with your own web analytics (like Google Analytics), you can verify that the integration is working properly and data is being passed correctly.
Detect technical problems
A/A testing can uncover technical issues like broken browser cookies, flawed randomization algorithms, or errors in statistical calculations that could impact testing results.
Establish baseline conversion rates
The performance data from an A/A test provides a baseline conversion rate for important pages and funnels on your site. This allows you to better assess the impact of future A/B test changes.
Determine sample size
A/A testing can help you identify the appropriate sample sizes to use for different tests based on the variance you see between identical variants.
Benchmark page/funnel performance
Beyond baseline rates, A/A testing gives you benchmark data on the broader performance of specific pages and funnels for comparison to future A/B tests.
When Should You Use A/A Testing?
Here are some common situations where running A/A tests can be beneficial:
- When starting to use a new A/B testing tool or platform
- After making any major changes to your existing A/B testing setup
- When releasing a new website or app
- If you notice inconsistencies between your A/B testing and analytics data
- Before running a highly important or expensive A/B test
- Periodically as an ongoing quality check (every 2-3 months)
Generally speaking, A/A testing is most useful during initial setup and when changes are made. Ongoing periodic tests help ensure your tools continue to function properly over time.
How to Set Up an A/A Test
If you’re ready to run your first A/A test, follow these steps:
1. Identify a test page
Choose an important page like your homepage or a key landing page for testing. This should be live page traffic you can split.
2. Create identical variants
Make 2+ identical copies of the page to use as variants. These must be exactly the same.
3. Determine sample size
Use a statistical significance calculator to determine the sample size needed. A/A tests often need more traffic due to lack of variance.
4. Split traffic evenly
Split your traffic evenly between the variants using your A/B testing tool. Use randomization to assign visitors.
5. Track KPIs
Identify key metrics like conversion rate to track for each variant. Use your analytics integration to compare data.
6. Run the test
Run the A/A test until the predetermined sample size and time duration is reached.
7. Analyze results
If there’s no significant difference in the metrics between variants, the test is a success! Any variance may indicate a potential issue.
Be sure to actively monitor the test and results as it runs. The longer it runs, the more data you’ll have to ensure the accuracy.
Real-World A/A Testing Examples
To understand how A/A testing is used in reality, let’s look at a few examples:
Testing a new A/B tool
Company X just integrated a new A/B testing tool. They want to verify it’s working correctly before relying on results. They run an A/A test on their pricing page and see identical conversion rates between variants – success!
Changed analytics setup
Company Y recently migrated to a new analytics platform. They run an A/A test on their homepage and compare conversion rate data between their A/B tool and new analytics. The data matches up, confirming the tools are integrated properly.
Suspicious test results
Company Z concludes a test on their checkout flow but the “winning” variation oddly reduces average order value. They setup an A/A test on the checkout page which reveals a discrepancy between variants. This points to a potential issue with their testing tool that needs to be fixed before further testing.
Ongoing quality assurance
Company A runs A/A tests on a few important site pages every quarter. Even if nothing seems broken, this periodic check gives them confidence in their tools and reassures them that technical issues aren’t skewing results.
Wrap Up and Key Takeaways
Here are the key points to remember about A/A testing:
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A/A testing splits traffic between identical variants to check for issues.
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It validates A/B testing tools are working accurately before making business decisions.
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A/A tests require larger sample sizes and take more time than A/B tests.
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Run A/A tests when setting up new tools, making changes, or periodically to benchmark performance.
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Compare A/A test data across your A/B tool and analytics for full validation.
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Any significant variance between A/A variants implies a potential problem to address.
While A/A testing requires some additional effort, it provides invaluable quality assurance. Periodic A/A tests can catch issues early and ensure you can trust the results of your A/B tests. Integrating both methodologies will take your testing program to the next level.
Requirement of a large sample size
One problem with A/A testing is that it can be time-consuming. When testing identical versions, you need a large sample size to find out if A is preferred to its identical version. This, in turn, will take too much time.As explained in one of ConversionXLâs posts, âThe amount of sample and data you need for an A/A test to prove that there is no significant bias is huge by comparison with an A/B test. How many people would you need in a blind taste testing of Coca-Cola (against Coca-Cola) to conclude that people liked both equally? 500 people, 5000 people?â. Experts at ConversionXL explain that the entire purpose of an optimization program is to reduce wastage of time, resources, and money. They believe that even though running an A/A test is not wrong, there are better ways to use your time when testing. In the post, they mention, âThe volume of tests you start is important but even more so is how many you *finish* every month and from how many of those you *learn* something useful from. Running A/A tests can eat into the ârealâ testing time.â
Checking the accuracy of an A/B testing tool
Organizations who are about to purchase an A/B testing tool or want to switch to a new testing software may run an A/A test to ensure that the new software works fine and has been set up correctly.
âA/A testing is a good way to run a sanity check before you run an A/B test. This should be done whenever you start using a new tool or go for a new implementation. A/A testing in these cases will help check if there is any discrepancy in data, letâs say, between the number of visitors you see in your testing tool and the web analytics tool. Further, this helps ensure that your hypothesis is verified.â
In an A/A test, a web page is A/B tested against an identical variation. When there is absolutely no difference between the control and the variation, it is expected that the result will be inconclusive. However, in cases where an A/A test provides a winner between two identical variations, there is a problem. The reasons could be any of the following:
- The tool has not been set up correctly.
- The test hasnât been conducted correctly.
- The testing tool is inefficient.
Hereâs what Corte Swearingen, Director, A/B Testing and Optimization at Americaneagle.com, has to say about A/A testing:
âI typically will run an A/A test when a client seems uncertain about their testing platform, or needs/wants additional proof that the platform is operating correctly. There is no better way to do this than to take the same page and test it against itself with no changes whatsoever. Weâre essentially tricking the platform and seeing if it catches us! The bottom line is that while I donât run A/A tests very often, I will occasionally use it as a proof of concept for a client, and to help give them confidence that the split testing platform they are using is working as it should.â
A/A Testing vs A/B Testing vs A/B/n Testing
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