A/B testing is an essential part of running an e-commerce website that works as efficiently as possible.
Pitting different variations of a web page or campaign against one another can help you achieve your desired outcome much more consistently (a.k.a. increase conversion rates).
Instead of guessing about what copy or image will get more conversions, you can test hypotheses and gather hard data.
In this comprehensive guide, we’ll focus on how to interpret A/B test results and use the insights you gain to improve your conversion rates and revenue.
Let’s get started!
What is A/B testing?
First, let’s define A/B testing.
A/B testing (or split testing) is a systematic method of comparing two versions of a product page, landing page, other web pages, or a marketing campaign to determine which one performs better.
The two pages might feature a different headline or call-to-action, and A/B testing allows you to compare how these different elements resonate with your target audience.
Practically speaking, split testing requires splitting website visitors into two or more groups (i.e. control group and group B) and then showing one version of the web page to each group. Then, the quantitative data that’s collected about each version’s success rate can be compared to determine the winning variation.
A/B tests can investigate how different variants perform on several key metrics, including conversion rate, click-through rate, and revenue generated.
A step-by-step guide to conducting A/B testing
Although A/B testing can seem complicated, there’s actually a simple step-by-step process that you can use to generate reliable data about your website’s performance.
Here are the six steps you should follow in your future tests.
Step 1: Analyzing your website
You’ll want to start by conducting a thorough assessment of your website’s performance. By figuring out where most of your potential customers are leaving your sales pipeline, you’ll have a better idea of where to focus your A/B testing efforts.
For example, if you’re running effective paid ads on Google or social media platforms, but find that much of your incoming traffic is bouncing from your landing page, that’s a great place to start A/B testing.
Take a good look at your Google Analytics account and figure out how your web traffic tends to move through your site.
Step 2: Brainstorming ideas
On the pages that you’ve identified as a priority for testing, take a look at all the elements that might be contributing to your poor results.
Is there a headline that could be improved? Could your call-to-action be stronger?
Use a brainstorming session to come up with creative, data-driven ideas for making changes to your website.
Step 3: Prioritizing ideas
After generating a list of ideas, you’ll want to prioritize them based on their potential impact and their feasibility.
If you have the resources to use the multivariate testing method, you can test a few ideas at once.
If you’re using traditional A/B testing, on the other hand, you should start with the optimization ideas that are the highest on your list and move down from there.
Step 4: Creating challenger variants
You (or your marketing team) will then need to create test variations of the elements that you’re trying to optimize.
The same web page can perform very differently based on small changes to elements like your headline, so create many different versions of whatever you’re testing and then narrow it down to the best two or three options.
Step 5: Running the test
Now it’s time to execute the A/B test by randomly assigning users to the control and challenger variants.
Half of your visitors should see the original version of your page or ad campaign (control page), and the other half should see one of the new variations.
Make sure the test duration is long enough to generate an adequate sample size. Exactly how long you’ll have to run your tests depends on how much traffic you have arriving on your web page.
Step 6: Analyze AB test results
Finally, you need to analyze the data generated by the A/B test to draw conclusions and make informed decisions based on the results.
We’ll cover the ins and outs of this process in the next sections.
Watch this video to learn more about A/B testing and see a step-by-step guide on how to run A/B tests with OptiMonk:
What is A/B testing analysis?
A/B testing analysis is a process that involves using statistical methods to evaluate data collected from an A/B test to determine the most effective version.
It’s crucial to choose the appropriate statistical tests based on the type of data being analyzed and the specific research question being addressed to ensure the accuracy and reliability of the results.
The importance of A/B testing analysis lies in its ability to help organizations determine the effectiveness of their optimization efforts.
Through thorough analysis of A/B test results, you can understand whether the alterations made, such as modifications to call-to-action buttons or website content, have had the desired impact on key metrics.
3 types of A/B testing analysis
There are three levels to analyzing A/B test results, starting with basic analysis.
1. Basic analysis
Basic A/B testing analysis has two goals: first, to assure that your test has generated statistically significant results, and second, to establish which version is the winning variation.
Statistical significance measures the probability that the result you’ve observed is not merely due to chance, but rather represents a significant difference between the two versions you’re testing. A low significance level might occur if only a few visitors have seen the different variations of your site, making it more likely that the outcome is random and not a valid reflection of any differences between the two versions.
You’ll need to have an adequate sample size in order to get accurate results, and then you’ll be able to discover whether you’ve managed to achieve statistically significant results.
You should compare the performance of the control version to the performance of the challenger version. The winning variation will have performed better on the KPI that you’re concentrating on (usually conversion rate).
2. Secondary metrics analysis
Often, it’s a good idea to analyze your results using multiple metrics rather than just one KPI. You might find that one of your losing variations has a slightly lower conversion rate, but outperforms the others on metrics like time spent on site or revenue generated.
You might want to continue testing until you find a version of your webpage that performs well on all of the metrics you care about. After all, consumer behavior is complex, so you’re going to need more than one number to fully understand it.
3. Audience breakdown analysis
Finally, you can get deeper insights into visitor behavior by segmenting your audience based on demographics, behavior, or other factors. This can help you understand how your subgroups respond differently to the different variations.
For example, it’s usually a good idea to compare test results for mobile versus desktop users, since mobile users often respond very differently to design choices.
Once again, you need to be careful to pay attention to the statistical confidence level in each of these test results. You might have achieved a high enough sample size for your basic analysis to be statistically significant, but this doesn’t mean your sample size of mobile visitors is large enough to draw conclusions at a high level of confidence.
Failing to take these factors into account can lead to false positives and mistakes.
How to analyze your A/B testing results?
As you go over your A/B test results, you want to proceed from the more basic types of analysis to more complicated ones.
Here’s an easy-to-follow procedure you can use:
1. Check for statistical significance and winning variant
The first thing you’ll want to do is perform statistical tests to see whether your A/B test achieved a large enough sample size.
The sample size is a crucial factor in A/B testing analysis. It plays a significant role in the reliability and accuracy of the experiment results.
A sample size that is too small can lead to inconclusive findings, making it challenging to draw valid conclusions. On the contrary, a large sample size can highlight minor differences as statistically significant, which may not be practically relevant.
Luckily, most A/B testing tools will automatically perform this analysis, so all you have to do is check out your dashboard.
Next, you can confirm which variation is the winner (i.e. which variation performed best in terms of your primary metric). This gives you a leading candidate for the version you’ll eventually roll out to all your site visitors at an early stage of the analysis.
2. Compare your test results across multiple KPIs
Next, you should examine your test results on many different measures of customer behavior.
Although comparing conversion rates is essential, especially since they’re usually the KPI used as the primary metric for A/B testing, it’s not enough on its own.
You should consider other factors in order to gain a well-rounded understanding of your web pages and marketing campaigns, including your click-through rate, time spent on page, and revenue generated.
When deciding on the metrics for an A/B test, it’s important to align them with your specific goals and objectives for the test.
The chosen metrics should be relevant to the test, measurable, and directly linked to the goals you have set for the experiment.
By selecting metrics that are meaningful and aligned with your desired outcomes, you can effectively evaluate the success of your A/B test and make informed decisions based on the results.
3. Segment your audience for further insights
Segmentation is a powerful tool for understanding how different groups of users respond to your variations.
Once you’ve seen how your audience as a whole interacts with the different variations you’ve created, you can break your results down further.
For example, you might examine whether first-time visitors respond to a certain variation in a very different way than your returning visitors. These types of insights can help you personalize your website for different users’ preferences in the future.
4. Analyze external and internal factors
Now that you’ve analyzed the data you’ve collected from an A/B test, you also need to consider external variables that might have affected your results.
For instance, if you run an A/B test during the busy holiday season or right after one of your competitors goes out of business, your results might not be an accurate representation of what you’d see at a different time of the year. While it’s impossible to control everything, you should try to ensure your test succeeds in creating results that you can generalize.
If you think that your A/B testing results have been seriously affected by external factors, it might be a good idea to repeat your test to see if you get comparable results.
5. Review click and heatmaps
Click and heatmaps offer a visual representation of how users interact with a web page.
You can gain valuable insights by taking a look at how users tend to navigate through your page. One variation might lead to very different user behavior than another one, and this might affect your desired outcome in a way that doesn’t show up in the quantitative data.
If your A/B testing tool doesn’t automatically include a heat mapping or session recording feature, you can use a dedicated tool like HotJar.
6. Take action based on your results
At the end of this process, you’ll know whether the new variations you tried out as part of your A/B test have gotten the results you’re looking for.
If you haven’t found that there’s a significant difference between the variations you tested, that’s perfectly fine because there’s nothing wrong with testing out a wrong hypothesis. That’s exactly what testing is for! In that case, you might decide to stick with your original design or choose to continue iterating and try to find something that results in an improvement.
On the other hand, if you’ve found a new variation that crushes what you were doing before, you’ll probably want to pull the trigger on rolling it out for your entire audience. Congrats!
There are so many different outcomes at this stage that we can’t really cover them all. For instance, if you have a variant with a great conversion rate but a mediocre revenue-generated number, you might want to keep testing, but that’s always a tough decision.
The best advice is to consider A/B testing as part of a broader, continuous process of conversion rate optimization and keep refining your website indefinitely.
Automate your A/B testing process with AI
As you’ve seen, A/B testing can be a complex process that requires time and effort, not to mention all the CRO knowledge you’ll need.
That’s why small brands and marketers have struggled to experience the full benefits of A/B testing—until now.
OptiMonk’s Smart A/B Testing tool allows you to fully automate your A/B testing process: it creates variants for you, runs the tests, and analyzes the results without your involvement.
You simply choose the elements you want to optimize on your landing pages, and then the AI works its magic!
With Smart A/B Testing, you can say goodbye to manual work and embrace data-driven A/B testing.
Wanna give it a try? Click here.
FAQ
What is statistically significant A/B testing?
Statistical significance indicates that the differences observed between variations are unlikely to have occurred due to chance. If you haven’t achieved statistically significant results, your winning variation will probably not perform as well as you thought over a longer period of time. That’s because its success was down to chance rather than a significant, meaningful difference. You reach statistical significance by gaining a large enough sample size to be sure that you have accurate results.
How do you interpret A/B testing results?
Interpreting A/B testing results involves looking beyond statistical significance. Consider segmenting your data, analyzing external and internal factors, and reviewing user behavior through click and heatmaps to draw meaningful conclusions.
Can you run an A/B test with unequal sample sizes?
Yes, it is possible to conduct an A/B test with unequal sample sizes. However, it’s important to note that using unequal sample sizes is not considered ideal for conducting such tests. Unequal sample sizes have the potential to impact the statistical power of the test and the precision of the results obtained. For optimal reliability and accuracy of the outcomes, it’s generally recommended to strive for sample sizes that are either equal or very similar in magnitude when running an A/B test.
How long should an A/B test run to ensure reliable results?
A/B tests should typically run for a sufficient duration to gather an appropriate sample size that can provide statistically significant test results. One important consideration is the statistical significance level, which indicates the confidence in the conclusions of the test. It’s generally suggested to continue running A/B tests until at least one variation reaches a significant level of 99%. This threshold suggests that the observed differences in performance between the variations are unlikely to have occurred by random chance alone. Therefore, by waiting until this level of significance is reached, you can ensure that the results observed are reliable and actionable.
Wrapping up
Whether you’re rolling out a new landing page or trying to refine the subject line of an email marketing campaign, A/B testing is a crucial tool for creating optimized user experiences that lead to higher conversions and sales.
Testing is simply the only way to make decisions in a reliable, data-driven way.
But remember, your A/ B testing is only as good as your analysis of your results. That’s why understanding the significance level of your tests, looking at many different KPIs, and segmenting your audience is essential.
Once you know the ins and outs of this crucial stage of A/B testing, you’ll be able to achieve results you never thought were possible.
If you’d like to start A/B testing but avoid all the hassle, give OptiMonk’s Smart A/B testing tool a try!
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