As a website owner, it’s easy to assume that you know what your target audience wants, especially when many users visit your site regularly to consume your content.
However, this isn’t always the case. The only sure way to know what your audience responds to best is through continuous A/B testing. When done properly, A/B testing can benefit your business in many ways, including saving you time and money, improving user engagement, reducing bounce rate, and increasing conversions and profits.
This post explores the 6 most common mistakes that can make your A/B testing results invalid, so let’s dive in:
1. Calling off Your Tests Too Early
To achieve statistical significance, you must run your A/B test for a suitable period of time. The specified duration will depend on multiple factors, such as website traffic, test goals, conversion rates, etc.
Some marketers often make the mistake of calling off their A/B tests the moment they see a statistically significant result. They may need to learn that A/B test results change quickly. A variation that seems to have the highest conversion boost on day 1 may end up as the losing variant by day 9 or 10.
Therefore, when you call off your tests prematurely, it significantly increases your risk of getting a deceptive false-positive result, which won’t benefit your business at all.
So, you have to stay calm and allow your tests to run to the end if you want to get results that will bring actual improvements to your website.
2. Making Too Many Changes between the Control and the Variant
This is another common A/B testing mistake you should avoid. Testing too many variables against each other simultaneously may seem like a great way to get more insights to improve your website performance faster.
But, the truth of the matter is that when there are so many differences between your control and your variants, it becomes challenging, if not impossible, to pinpoint the exact change that led to the success or failure of your A/B test results.
For instance, if you serve half of your website visitors your current landing page as it is, while the other half gets a landing page where you’ve changed the color, shape, size, placement, and copy of your CTA buttons, you won’t be able to pinpoint the specific change that drove the results.
Also, the more variables you’re testing simultaneously, the more traffic you’ll need on the page to get statistically significant results, and the longer your tests will run. So, your best bet is to test only one variable against the control simultaneously.
3. Formulating an Incorrect Hypothesis
A hypothesis is a clearly defined and precise prediction formulated explicitly before conducting a test or experiment, like an A/B test. It summarizes what you’ll change, your expected outcomes, and why you believe this outcome to be the case. It enables you to answer what you hope to learn from the experiment.
When you begin your A/B test with an incorrect or invalid hypothesis, it significantly decreases your chances of succeeding in the tests. Many marketers often just guess their hypothesis because they have no idea how to come up with one.
If you’re in the same shoes, you should draw insights from surveys, Google Analytics, expert reviews, visitor recordings, and other research sources to create a strong hypothesis for your tests.
4. Scheduling Your Tests Incorrectly
Another common A/B testing mistake among marketers is scheduling tests incorrectly. For instance, you’ll find someone showing version A of their landing page to website visitors during the week and version B during the weekend.
These two testing periods are incomparable because the activity levels of website users can be very different during the week and weekend, affecting traffic. Therefore, your test results cannot be conclusive or reliable.
Because of this, industry experts recommend you simultaneously test both versions (the control and the variant). This is possible if you divide your website visitors into two groups and show them each version at the same time.
5. Using Faulty A/B Testing Tools
The type of A/B testing software you use also has a direct impact on your test results. Using well-established and high-quality A/B software increases your chances of succeeding in your tests and vice versa.
As a marketer, you have many options when it comes to selecting A/B testing software for your business. Testing software is available at a wide range of prices, quality, and available features. It’s important to know what you need, as you might buy faulty or low-quality software that will get you started on the wrong foot.
For example, if you end up with testing software that slows down your site significantly, it can seriously impact your website conversions. With slow response times, many impatient visitors may be bouncing to other sites. This scenario could even affect your overall SEO, page views, total sales, and profits in the long run.
So, how can you be sure that your testing tool is working well before you launch your A/B tests? Through A/A testing.
A/A testing refers to an experiment where you show two similar versions of a web page to two groups of website visitors simultaneously and compare their performances. In other words, you’re testing the same web page against itself.
The main reason for A/A testing is to check if your testing software is working fine and statistically fair. If you implement the test correctly, your software should not report any conversion differences between the variation and the control.
However, suppose you’re using a faulty tool. In that case, you’ll notice significant conversion differences between the two versions you’re testing, and as a result, you likely shouldn’t use it for your A/B testing.
6. Introducing Changes During Testing
The last A/B testing mistake that’s widespread among marketers is introducing changes while a test is ongoing. When they notice that things aren’t going their way, they rush to make changes hoping it will fix the issue.
Some of the most common changes they make include changing the experiment settings, changing their test goals, and changing the traffic allocations to the control or the variations.
It’s important to point out that making such changes in the middle of an A/B test introduces bias in your test results, making them unreliable. So, you should always let your tests run to completion. It’s the only way you’ll be able to get conclusive and reliable results.
This article doesn’t exhaust all the A/B testing mistakes marketers make out there, but it gives you some of the most common ones and the specific ways to prevent them. It’s your turn now to implement what you’ve learned on your website to boost your overall performance and conversions.