A/B testing is a method of experimentation where two or more variants of a product are shown to users at random, and statistical analysis is used to determine which variant performs better.
This technique can be used to test anything from website copy to product features and is a valuable tool for any company that wants to optimize its user experience.
A/B testing is an important part of any user experience optimization strategy. By testing different variants of your product, you can make informed decisions about which changes to make based on hard data.
A/B testing can be used to test anything that can be measured. Common things to test include:
There are a few key steps to setting up an A/B test:
Let's go into more detail on each of these steps.
Before you can start your A/B test, you need to define what you want to achieve with the test. This will help you choose the right metric to track and determine whether your test was successful.
Some examples of goals you might want to test for include:
Once you've defined your goal, you need to choose a metric to track. This metric should be directly related to your goal. For example, if your goal is to increase sign-ups, then your metric could be the number of sign-ups.
There are a few things to keep in mind when choosing your metric:
Now that you've defined your goal and chosen your metric, you're ready to set up your test. There are a few things you'll need to do:
In an A/B test, you need to have a control (the original version of your product) and a treatment (the new version of your product). For example, if you're testing different calls-to-action on your website, the control would be the original call-to-action and the treatment would be the new call-to-action.
Your sample size is the number of users you'll show your treatment to. This number will depend on a few factors, including:
You can use a sample size calculator to determine the appropriate sample size for your test.
Your test duration is the amount of time you'll run your test for. This will depend on a few factors, including:
A general rule of thumb is to run your test for a minimum of two weeks.
Now that you've set up your test, it's time to run it. There are a few things you'll need to do to make sure your test is successful:
There are a few different platforms you can use to run your A/B test. Some popular options include:
Once you've chosen your platform, you'll need to implement your test. This will involve adding some code to your website or application.
If you're using Google Optimize, you can use the following code to implement your test:
<!-- Google Optimize Container -->
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-XXXXXXXX-X', 'auto');
ga('require', 'GTM-XXXXXXX');
ga('send', 'pageview');
</script>
<!-- End Google Optimize Container -->
If you're using Optimizely, you can use the following code to implement your test:
<!-- Optimizely Container -->
<script src="https://cdn.optimizely.com/js/XXXXXXXXXX.js"></script>
<!-- End Optimizely Container -->
If you're using Visual Website Optimizer, you can use the following code to implement your test:
<!-- Visual Website Optimizer Container -->
<script type='text/javascript'>
var _vis_opt_account_id = XXXXXXXXX;
var _vis_opt_protocol = (('https:' == document.location.protocol) ? 'https://' : 'http://');
document.write('<s' + 'cript src="' + _vis_opt_protocol +
'dev.visualwebsiteoptimizer.com/deploy/js_visitor_settings.php?v=1&a='+_vis_opt_account_id+'&url='
+document.URL+'&random='+Math.random()+'" type="text/javascript">' + '<\/s' + 'cript>');
</script>
<!-- End Visual Website Optimizer Container -->
Once your test is running, you'll need to monitor it to make sure everything is working as expected. This includes monitoring your test platform and your test results.
Once your test is complete, it's time to analyze your results. This will involve looking at your metric data and determining which variant performed better.
There are a few things to keep in mind when analyzing your results:
If your results are not statistically significant, it means that the difference between the variants is not statistically significant. This could be due to a number of factors, including a small sample size or a low conversion rate.
If your results are not reliable, it means that there is a chance that the results are not accurate. This could be due to a number of factors, including a change in user behavior or a change in the test platform.
If your results are not actionable, it means that there is no clear winner and you cannot make a decision based on the results. This could be due to a number of factors, including a small difference between the variants or a low conversion rate.
A/B testing is a valuable tool for any company that wants to optimize its user experience. By testing different variants of your product, you can make informed decisions about which changes to make based on hard data.
When setting up your A/B test, there are a few things you'll need to do:
If you follow these steps, you'll be well on your way to running a successful A/B test.