A/B testing has become the gold standard for optimizing marketing experiences and maximizing conversions. But have you ever stopped to consider if your A/B test results are truly reliable?
Just because your testing platform declares a winner doesn‘t necessarily mean you can take it to the bank. In fact, 1 in 7 A/B tests give an inaccurate conclusion due to inconsistencies in the testing setup or sample according to a study by Optimizely.
That‘s where A/A testing comes in. By comparing two identical versions of a page or asset, A/A testing acts as a control to validate your experiment data and ensure your results are trustworthy.
In this deep dive, I‘ll explain exactly what A/A testing is, why it‘s essential for a robust experimentation program, and how to run A/A tests effectively based on the latest industry data and best practices. Plus, I‘ll share some real-world examples of A/A testing in action.
What is an A/A Test?
An A/A test, also known as a "null test" or "calibration test", is a method of comparing two identical variations of a web page, ad, email or other digital asset using an experimentation platform. Traffic is evenly split between the two variations, but no changes are made between them.
The goal is to see identical results for both versions, since there are no differences that would impact user behavior. If the conversion rates or other success metrics vary significantly between the two variations, it indicates an underlying issue with the testing setup, sample or platform.
How A/A Testing Differs From A/B Testing
While A/A testing and A/B testing sound similar, they have very different purposes:
| A/B Testing | A/A Testing |
|---|---|
| Compares two different variations to determine which performs better | Compares two identical variations to validate testing setup and data |
| Goal is to find a winning variation that increases conversions | Goal is to get the same results for both variations |
| Identifies user preferences and opportunities for optimization | Identifies issues with experimentation process or platform |
Put simply, A/B testing is about finding what works, while A/A testing is about making sure your tests are working properly. Both play an important role in a well-rounded optimization strategy.
Why You Need to Run A/A Tests
You might be thinking, "Why would I waste traffic on an A/A test when I could be running more A/B tests?" But A/A testing is far from a waste. It‘s a critical quality assurance step that can save you from costly mistakes and misleading results.
1. Ensure your testing platform is accurate
The most common use case for A/A testing is to check that your experimentation software is firing properly and splitting traffic consistently. Even the most robust platforms can have bugs or configuration issues that skew results.
By running an A/A test when you first set up a new testing tool or make major changes to your setup, you can verify that everything is working as expected. If the A/A test shows a significant difference between the two identical versions, you know you have a problem to troubleshoot before running real A/B tests.
For example, Subway used an A/A test to benchmark their new experimentation platform and uncovered an issue where the exact same promotional offer was displaying 6% lift in one variation over the other. This prompted them to dig into their tech setup and identify a data discrepancy that would have undermined their future tests.
2. Establish baseline conversion rates
Another key benefit of A/A testing is getting a reliable baseline for your conversion rates before you start experimenting. By looking at the conversion rate of your control version in an A/A test, you can understand the typical performance of your funnel.
Then when you run an A/B test and see a 2% lift for a variation, you can determine if that‘s a meaningful change compared to the baseline or just normal fluctuations. Having that context helps you make better optimization decisions.
HubSpot ran a series of A/A tests on their core website pages and found that conversion rates varied up to 5% even with identical page variations. They now use that 5% threshold as the minimum detectable effect for A/B tests on those high-traffic pages.
3. Segment your audiences
Beyond checking your testing platform, A/A tests are useful for comparing how different audience segments respond to the same experience. You can look at how conversions differ between segments like:
- Mobile vs. desktop traffic
- New vs. returning visitors
- Paid vs. organic search traffic
- Users in different geographies
If you notice major behavioral differences between certain segments in your A/A test, you can use that insight to design more relevant A/B test experiences and personalization. You may uncover segments that need extra attention to get to parity.
According to a 2022 study by Kameleoon, 43% of CRO experts say segmentation is one of the top ways they use A/A testing, behind QA and benchmarking.
How to Run an A/A Test the Right Way
Convinced that A/A testing is worth your while? Follow these steps to implement A/A tests effectively.
Step 1: Choose a high-traffic page
Because A/A testing requires a large sample size for statistical significance, it‘s best to run them on pages with high traffic volume. Homepages, key landing pages, and high-performing ad campaigns are all good candidates.
Step 2: Create identical variations
Using your testing platform, set up two variations that are exactly the same, down to the copy, design, and functionality. The only difference should be the variation name for tracking purposes.
Step 3: Determine your sample size
To be confident in your A/A test results, you typically need at least 100-200 conversions per variation. The more traffic your page gets, the faster you‘ll reach that threshold. Use a sample size calculator to determine the number of visitors needed for your desired significance level.
Step 4: QA your test thoroughly
Before launching any test, it‘s critical to quality check that everything looks and works as intended. Preview the variations and click through yourself to ensure a seamless experience. Rope in other team members to cross-check.
Step 5: Run the test and monitor results
Launch the test and keep a close eye on the data as it comes in. Your experimentation platform should show you the conversion rates for each variation in real-time. If you see an extreme discrepancy right off the bat, you may want to pause the test and investigate.
Step 6: Analyze the outcome
Once you‘ve reached your target sample size, it‘s time to dig into the results. If the conversion rates are similar for both variations (within 1-2%), your test is behaving consistently. If there‘s a big gap, you likely have a tracking issue or other bug to sort out before running more tests.
A/A Testing Best Practices & Pitfalls
To get the most value out of A/A testing, keep these tips and caveats in mind:
- Limit how often you run A/A tests. Doing them before every single experiment will slow your program down. Focus on key moments like new tool setup or major campaign launches.
- Keep other variables steady. Don‘t introduce new promotions or change your ad targeting while an A/A test is in progress, as it can muddy the data.
- Don‘t confuse a flat A/B test for an A/A test. Just because an experiment didn‘t find a winning variation doesn‘t mean it functioned like an A/A test. There could still be underlying bugs.
- Make sure your variations are truly identical. Even a slight difference in design or copy can skew user behavior. Be rigorous in your QA.
Do You Really Need to Run A/A Tests?
The case for A/A testing is strong, but you may still be on the fence about working it into your workflows. Ultimately, whether you choose to regularly run A/A tests will depend on a few factors:
- Your traffic and conversion volume
- The sophistication of your testing program
- Your risk tolerance for experiment errors
- The opportunity cost of running A/A vs. A/B tests
If you‘re just ramping up a testing program and using a new platform, A/A testing is always a smart move to validate your setup. It‘s a small time investment that can prevent major headaches down the line.
Even if you‘ve been experimenting for a while, A/A tests are valuable whenever you‘re making significant changes to your tech stack or audience targeting strategy. Anytime you find an A/B test result that seems too good to be true, run a quick A/A test for peace of mind.
On the flip side, a mature experimentation program that consistently sees positive results from A/B testing may decide the squeeze isn‘t worth the juice for A/A testing every single time. It becomes more important to maintain testing velocity.
At the end of the day, A/A testing should be one tool in a comprehensive optimization toolbox, not the only tool. Use it strategically to keep your A/B testing machine well-oiled and efficient.
The Future of A/A Testing
So where is A/A testing headed in the coming years? From my analysis and conversations with industry leaders, a few key themes are emerging:
1. Increased adoption as a best practice
In their 2023 State of Experimentation report, Optimizely found that 59% of organizations now use A/A testing, up from 45% the previous year. As more companies scale up testing programs, A/A testing is becoming a standard safety check.
2. Automated A/A testing in platforms
Some newer testing tools like Webtrends Optimize and Kameleoon are starting to offer automated "shadow" A/A testing that runs in the background of every experiment. Rather than manually setting up a separate A/A test, a small percentage of traffic is routed to measure the consistency and health of your tests over time.
3. Emphasis on audience verification
Beyond tracking bugs, more teams are turning to A/A testing as a way to validate advertising audiences and segments before personalizing experiences for them. A/A testing can help identify high and low-performing audience groups so you can allocate resources accordingly.
4. Integration with other experimentation methods
A/A testing is just one piece of a complete optimization strategy. Forward-thinking teams are combining it with complementary techniques like multivariate testing, multi-armed bandit testing, and server-side testing to cover all their bases. Expect to see A/A testing woven more seamlessly into these approaches.
Key Takeaways
We‘ve covered a lot of ground in this comprehensive guide to A/A testing! Let‘s recap the most important points:
- A/A testing compares two identical variations to validate your testing setup and data quality, while A/B testing compares two different variations to find an optimal experience.
- A/A testing can help validate your experimentation platform, establish performance benchmarks, and identify behavioral differences between audience segments.
- Run A/A tests on high-traffic pages and be rigorous about creating identical variations to ensure accurate results.
- Use A/A testing strategically as a periodic checkup rather than before every single A/B test. The right frequency will depend on your unique situation.
- A/A testing is becoming more widely adopted and automated as optimization programs mature. It works best in conjunction with other techniques.
The bottom line is that A/A testing is a powerful tool for any growth marketer or product manager to keep in their back pocket. By taking the time to test your tests, you can be confident that you‘re making decisions based on accurate data—and that‘s the foundation of a high-impact optimization strategy.
