Why you should run A/A tests in your conversion optimization program
Did you know anywhere from 80% to 90% percent of A/B tests do not produce any significant results?
Only 1 out of 8 A/B tests drive significant change.
Done incorrectly, some marketers have begun to question the value of A/B testing…
…their A/B test reports an uplift of 20% and yet, the increase reported by the AB testing software never seems to translate into improvements or profits.
“Most winning A/B test results are illusory.” (Source: Qubit)
Furthermore, the majority of arguments that call for running A/A testing consider it a sanity check before you run an A/B test.
In this post, we’ll examine arguments around that (for it and against it)…but we will also suggest other ways to look at A/A tests and why we run them regularly on our CRO projects.
Let’s dive right in!
What is an A/A test?
A/A testing is a strategy of testing two identical versions of the same webpage, against each other.
You can think of A/A testing as an A/B test that’s supposed to return flat results.
In theory, A/A tests are designed to help marketers examine the reliability of the A/B testing tool used to run A/B tests, with the goal being: “to find no difference between the control and variant.”
Now, since you’re running the original page against itself (or multiple versions of itself), it will be logical that visitors will react the same way to all the different test recipes and thus, the A/B testing software will not be able to declare a winner.
The theory is that, the difference in conversion rates between variations will not reach statistical significance.
Other versions of A/A tests include running an AABB test. In this case, you will have the control, an identical copy of the control, a challenger and an identical copy of the challenger.
Then, you will run your A/B test as you usually do with an original and a challenger, but you will also add two sanity check versions to measure the accuracy of the testing software on the results.
How are A/A tests implemented?
Running an A/A test is much like running A/B tests, except in this case, the two groups of users which are randomly chosen for each variation, are given the exact same experience.
- Two groups of users are provided with absolutely identical high traffic web pages
- The user experience of both of these groups is similar.
- It is expected that KPI (Key Performance Indicator) will also be the same for two groups
- If the KPI does not match, then one should analyze the exact reasons for the unexpected result
Note: You will also want to integrate your AB testing tool with your analytics so that you can compare conversions and revenue reported by the testing tool to that reported by analytics – they should correlate.
Should you run A/A tests?
To A/A test or not is a question that invites conflicting opinions. There are companies that include running an A/A test as part of any engagement, others consider a complete waste of time and resources.
Arguments for running AA tests
Companies that recommend [and practice] A/A testing usually run these tests when testing a new A/B testing tool or want to start a new implementation.
It may not be worth optimizing every minor tweak to your website or project as it will slow down your workflow.
For example, low-traffic tests could take weeks to complete.
Likewise, usually, it isn’t worth to repeatedly calibrate your testing tools.
The best time to run an A/A test is when you’ve integrated a new tool – if everything is in working order, you can safely move on to A/B tests.
In fact: The only times you should consider running an A/A test is when:
- You’ve just installed a new testing tool or changed the setup of your testing tool
- You’re starting a new project
- You find a difference between the data reported by your testing tool and that reported by the analytics
Arguments against A/A Testing
There are three main arguments against running AA testing:
1. Running an A/A test is waste of time and resources that you could use for something that generate better ROI
Craig Sullivan, who is one of the early CROs, doesn’t recommend A/A testing.
Not because he thinks it is wrong, rather;
Just that my experience tells me that there are better ways to use your time when testing. The volume of tests you start is important but even more so, is how many you *finish* every month and how many from those that you *learn* something useful from. Running A/A tests can eat into ‘real’ testing time.
The issue is not a philosophical one for Craig, but one around practicality. And in industry focused on delivering the most value for clients, this makes total sense.
While you could technically run an A/A test in parallel with an A/B test, doing so would make the process more statistically complex.
It will take longer for the test to complete, and you’ll have to discard your A/B test’s results if the A/A test shows that your tools aren’t properly calibrated.
2. Declaring a winner in a A/A test does not tell you a lot
The concept of confidence is Inherent in any type split or multivariate testing. The fact that an A/B testing engine declares a winner with 99% confidence does not mean that you are certain that you found a true winner.
A statistical significance of 95% means that there is 1 in 20 chance that the results you’re seeing in your test are due to random chance.
As a matter of fact:
After running thousands of A/B tests and hundreds of A/A tests, I came to expect to see different testing platforms declare a winner in an A/A test regularly. I have seen this in Test & Target, Google Website Optimizer (while it lasted), Optimizely and VWO.
3. A/A tests require a large sample size to conclude
Final argument against running A/A tests is that they require a large sample size to prove that there is no any significant bias.
Here’s a vivid example Qubit shared in their phenomenal white paper titled “Most Winning A/B Test Results Are Illusory:”
Imagine you are trying to find out whether there is a difference between the heights of men and women.
If you only measured a single man and a single women you would stand a risk that you don’t detect the fact that men are taller than women.
Why is this? Because random fluctuations mean you might choose an especially tall woman or an especially short man, just by chance.
However, if you measure, say, 10000 people, the average for men and women will eventually stabilize and you will detect the difference that exists between them. That’s because statistical power increases with the size of your sample.
If this was an A/B test, we would have gone ahead with the “women are taller than men” variant. This wouldn’t have been a major loss, but it wouldn’t have been a win either!
The scary part is the same thing can happen when the variant is actually worse, meaning, you can move forward with a “winning” variant, and watch your conversion rate drop!
A different way to think about A/A testing
At Invesp, we run A/A tests as part of any CRO services. We typically run these tests at the start of the project, and then every 4 to 6 months for the first 1-2 weeks as we are gathering different data on the website and its customers.
1. We want to benchmark the performance of different pages or funnels on the website
How many visitors or conversions come to the homepage, cart page, product pages, etc.
When we do that, we are not worried about whether we are going to find a winner or not, we are looking for general trends for a particular page.
These tests help us understand questions such as: What is the macro conversion rate for the home page?
How does that conversion rate breakdown between different visitor segments? How does that conversion rate breakdown between different device segments?
A/A tests provide us with a baseline that we can take a look at as we are preparing new tests for any part of the website.
One might say that we can get the same data from the analytics platforms on the website.
Yes, and No!
Since our A/B testing tool is what we mainly use to declare a winner (while still sending data to Google analytics or doing external calculations), we still want to see the website metrics in using the A/B testing tool.
2. We decide a minimum sample size and expected time to run a test
Determining required sample size is very important for an A/B test
If the sample size is too small, not much information can be obtained from the test in order to draw meaningful conclusions…
…on the other hand, if it is too large, the information obtained through the tests will be beyond that needed, thus time and money are wasted.
When we conduct an A/A test for different areas of the funnel, we look closely at the number of visitors the A/B testing platform is capturing, the number of conversions, conversion rates, etc.
All of this data is used to help us determine the minimum sample size required to run an A/B test on a particular funnel on the website. It also helps us determine how long we need to run our regular A/B tests for.
3. We want to get a general sense of how long it takes to deploy the simplest, straightforward A/B test on the website
You have to agree that an A/A test is the easiest and fastest test you can deploy on a website. It is amazing how many technical challenges appear when you run a simple A/A test. This always the case if the client is just starting out with a CRO project. They have never deployed a test on their website. The more complicated the technical architecture for the client website, the more AA tests will be helpful in identifying possible technical issues before we launch the actual program.
Scripts not installed correctly, GTM needs to be configured to capture additional data, issues around 3rd party conversions and the list goes on.
4. Never trust the machine: check the accuracy of the A/B testing tool
Before running A/B tests, it’s important to make sure your tools are configured correctly and working the way they should.
Running these tests helps us check the accuracy of the A/B testing tool we’re using.
Companies 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 the new software works fine, and if it has been set up properly.
I recall one project where all tests run by the client on a particular testing platform ended up with a loss. All 170 tests. Mind you, I am used to running tests that generate any improvement. But running 170 tests with no result is unusual.
When we switched the client to another platform and re-ran some of the 10 most promising tests, 6 of them resulted in winner with 99% confidence.
Chad Sanderson who runs CRO for subway had great insights into this:
It isn’t wise to downplay the danger of system errors. Most A/B testing solutions use slightly different algorithms that may or may not result in major discrepancies the harder the program is pushed (Think 10 – 20 – 30 variants). This might seem like an outlier issue, but it also might indicate a deeper underlying problem with either A.) the math B.) the randomization mechanism or C.) the browser cookie. Tools break (quite often actually) and putting blind trust in any other product is asking for trouble.
We have to admin: the reliability of your AB testing software is a scary thought. If you are using that AB testing software to determine the winner of your tests, and then you question the reliability of the software, you are effectively questioning your whole program:
Chad Sanderson adds:
Just because a program doesn’t generate an overwhelming amount of type I errors (95% confidence) doesn’t mean it still can’t be flawed. Thanks to the statistical mechanisms behind A/A tests (P Values are distributed uniformly under the null hypothesis) we can analyze test data the same way we’d determine whether or not a coin is fair or weighted: by examining the likelihood of observing a certain set outcomes.
For example, after flipping a fair coin 10 times we could expect to see 10 heads in a row only once out of 1024 attempts (50/50 chance per flip). In the same way, if we run a 10 variant A/A test and see all 10 values have a p value over .5, the probability of this happening would be the exact same (50/50 chance per test). Without going too deep into Bayesian statistics, the next step would be to ask yourself if it’s more likely that you observed a very rare result on your first attempt or that something is wrong with the tool.
If you’re a CRO, let me suggest a new idea. For the next month, do not rely on your A/B testing software to declare winners. Send your testing data to Google analytics, pull the numbers for each variation from analytics, and do the analysis yourself!
Considerations when conducting A/A testing
1. What should you do if your A/A test shows a winner?
When running an A/A test, it’s important to keep in mind that finding a difference in conversion rate between two identical versions is always a possibility.
This doesn’t necessarily mean the A/B testing platform is inefficient or poor, as there is always an element of randomness when it comes to testing.
Now what should you do if you find a winner?
Let me give you an example posted by Chad Sanderson using Adobe Target, running 10 Variants, all default vs default.
Time period being looked at here was 3 weeks (note the number of orders on the left and confidence to the right) on a Desktop – New Visitors – Cart segment. Running these through a one-tailed statistical calculator even with a strict family error correction still ends with 7 significant results.
If you used a Bayesian interpretation it would be 10/10. All variants had more than enough power. Yikes.
2. If you perform A/A tests and the original wins regularly:
There is a good chance that challengers are losing due to performance issues (it takes a little bit to load a variation and that delay works in favor of the original).
Therefore, evaluate your testing platform. We have seen this happen more with some testing platforms more than other platforms.
3. If you perform A/A tests regularly, and one of the variation/original wins:
It is a fact of life! It has been my experience that testing engines will declare a winner in 50% to 70% of AA tests (95% to 99% significance).
But then, look at the the kind of uplifters, downlifters are you seeing: If one of the variation wins with 3-4% regularly, that could be a signal that you should aim for uplifts higher than that when running your tests.
Chad Sanderson adds a great point:
Another great use of A/A Tests is as physical evidence either for or against testing a page or element. Let’s say you’ve run an A/A test and after one month have observed a difference between means that is still large, perhaps over 20%. While you could determine this just as easily from a sample size calculation, presenting numbers without context to stakeholders that REALLY want to run tests on that page may not get the job done.
It’s far more effective to show someone the actual numbers. If they see the difference between two variations of the same element is drastic even after a month, it’s far easier to understand why observing test results on such a page would mean only a result far greater than observed would be possible.
In this case, you’ll have the control, the challenger and an identical copy of the control.
Whether you believe in A/A testing or not…
you should always run a winner from an A/B test against the original in a head to head test to validate wins.
You run an A/B test with 4 challengers against original V2 wins with 5% uplift, then you create a new test with original against V2 in a head to head test.
Done correctly, A/A testing can help prepare you for a successful AB testing program by providing data benchmarks on different areas of the website, and check if there is any discrepancy in your data, let’s say, between the number of visitors you see in your testing tool and the web analytics tool.
Now, over to you.
What’s your perception on A/A tests? Are you running them on your website?
I’d love to hear your thoughts, so let me know in the comment section below!
Khalid Saleh is CEO and co-founder of Invesp. He is the co-author of Amazon.com bestselling book: “Conversion Optimization: The Art and Science of Converting Visitors into Customers.”
Khalid is an in-demand speaker who has presented at such industry events as SMX, SES, PubCon, Emetrics, ACCM and DMA, among others.
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