Multivariate vs A/B Testing: The Difference Explained
Posted: Wed Dec 04, 2024 3:46 am
Click here for a quick answer on the main difference between A/B testing and multivariate testing.
I'll give you the answer right away:
A/B testing is testing between two versions of your page (usually)
Multivariate testing is the testing of multiple variables and their combinations
If this doesn't satisfy your need for information, don't worry. I offer a more detailed comparison below.
A/B Testing Explained
A/B testing is a conversion rate optimization strategy where your goal is to find out which of two versions of your website performs better = converts more.
Typically, an A/B test involves two versions of your website or an element kenya phone number list of your website. These versions should only differ in one way so that we can see how this factor affects conversion rates.
In practice, this means that you have your original website, you create another version with a small change, and you ask yourself the question: does the new version perform better than the current version?
A/B testing
It could be the case
Which image works best
Which CTA button layout works best
Which review works best
And other details like that
If you start an A/B test with too many elements on the page, you won't be able to know which of them influenced the results of the experiment. The methodology isn't flexible enough to do that.
Definition of multivariate testing
Multivariate testing (MVT testing) is multiplied A/B testing.
Instead of two variables, you can examine different elements of the page and their combinations.
For example, you can choose three images and three different fonts and create a page version for each combination, or 9 different versions of the page.
multivariate testing
With this method, you will see how variables interact with each other: that is, which elements work best together.
Don't go crazy though, because the test will be too heavy and take too long.
In fact, multivariate testing is quite complex and time-consuming to perform, so you need to focus on the bigger picture.
Choose elements that really make a difference in your conversion rate, rather than fixing smaller details (leave that for A/B testing).
Practical cases for multivariate testing:
If you want to make radical changes
See how different elements work together
If you have several ideas for improvement and want to try them all at the same time
What is the difference between the two?
Technically, multivariate testing is a “subcategory” of A/B testing.
Again: The difference is that while A/B testing typically focuses on one variable at a time, multivariate testing (as the name suggests) is used to test multiple variables at the same time.
Please note! The example image below shows 9 different versions of a website, but that doesn't mean it has to look exactly like that for your test.
difference between A/B testing and multivariate testing
Check out the table for a more detailed comparison.
A/B MVT
Testing two variables of an element Testing combinations of multiple variables and elements
Good for testing details Good for bigger radical changes
It tells you which version is better It tells you which combination of elements is the best
It can be used for websites that have less traffic It requires a large amount of traffic to be split into multiple samples.
The results are easy to interpret. Analyzing the results can be difficult
The results are clear and specific Results may be ambiguous and the test may not produce meaningful results.
Quick to set up and run It is relatively difficult to set up and takes longer to complete.
Relatively simple methodology Complex methodology
Suitable for beginners Best for advanced optimizers
Similarities between the two
Now, let's see what the two have in common.
The similarities have more to do with the planning process, how to get the best results and how to react to them.
Before starting a test, analyze your problem
Determine a conversion goal
Formulate a hypothesis or several hypotheses
Carefully set up the test so that it measures what you want it to measure.
Drive enough traffic to each test page
Results must be statistically large to be significant.
After analyzing the results and confirming that they are accurate, implement changes
I'll give you the answer right away:
A/B testing is testing between two versions of your page (usually)
Multivariate testing is the testing of multiple variables and their combinations
If this doesn't satisfy your need for information, don't worry. I offer a more detailed comparison below.
A/B Testing Explained
A/B testing is a conversion rate optimization strategy where your goal is to find out which of two versions of your website performs better = converts more.
Typically, an A/B test involves two versions of your website or an element kenya phone number list of your website. These versions should only differ in one way so that we can see how this factor affects conversion rates.
In practice, this means that you have your original website, you create another version with a small change, and you ask yourself the question: does the new version perform better than the current version?
A/B testing
It could be the case
Which image works best
Which CTA button layout works best
Which review works best
And other details like that
If you start an A/B test with too many elements on the page, you won't be able to know which of them influenced the results of the experiment. The methodology isn't flexible enough to do that.
Definition of multivariate testing
Multivariate testing (MVT testing) is multiplied A/B testing.
Instead of two variables, you can examine different elements of the page and their combinations.
For example, you can choose three images and three different fonts and create a page version for each combination, or 9 different versions of the page.
multivariate testing
With this method, you will see how variables interact with each other: that is, which elements work best together.
Don't go crazy though, because the test will be too heavy and take too long.
In fact, multivariate testing is quite complex and time-consuming to perform, so you need to focus on the bigger picture.
Choose elements that really make a difference in your conversion rate, rather than fixing smaller details (leave that for A/B testing).
Practical cases for multivariate testing:
If you want to make radical changes
See how different elements work together
If you have several ideas for improvement and want to try them all at the same time
What is the difference between the two?
Technically, multivariate testing is a “subcategory” of A/B testing.
Again: The difference is that while A/B testing typically focuses on one variable at a time, multivariate testing (as the name suggests) is used to test multiple variables at the same time.
Please note! The example image below shows 9 different versions of a website, but that doesn't mean it has to look exactly like that for your test.
difference between A/B testing and multivariate testing
Check out the table for a more detailed comparison.
A/B MVT
Testing two variables of an element Testing combinations of multiple variables and elements
Good for testing details Good for bigger radical changes
It tells you which version is better It tells you which combination of elements is the best
It can be used for websites that have less traffic It requires a large amount of traffic to be split into multiple samples.
The results are easy to interpret. Analyzing the results can be difficult
The results are clear and specific Results may be ambiguous and the test may not produce meaningful results.
Quick to set up and run It is relatively difficult to set up and takes longer to complete.
Relatively simple methodology Complex methodology
Suitable for beginners Best for advanced optimizers
Similarities between the two
Now, let's see what the two have in common.
The similarities have more to do with the planning process, how to get the best results and how to react to them.
Before starting a test, analyze your problem
Determine a conversion goal
Formulate a hypothesis or several hypotheses
Carefully set up the test so that it measures what you want it to measure.
Drive enough traffic to each test page
Results must be statistically large to be significant.
After analyzing the results and confirming that they are accurate, implement changes