When you are in marketing or website development, there is one thing you need to understand: you cannot find perfection every time. This means that no matter how many tests you undertake, there will always be some space left for improvement. You will find more spaces for offer placements, better conversion rates, and even titles of your blogs that could perform better. In addition, there is no end to what type and how many times you can perform A/B testing on a single element. But there are multiple ways by which one can perform testing on their website. Two of the most common testing procedures are multivariate testing and A/B testing. Today, with the help of this article, we will show what makes these two tests utterly different from each other. Apart from this, we will also help you determine which option is better for you according to your requirements. So let’s begin.
Does Multivariate Testing Will Show Different Results Than A/B Testing?
Yes, these two are different forms of tests, and when you compare their results, each one will seem different. But that doesn’t mean you have to be cautious of one another. As we said, both of these tests have their benefits and disadvantages. In the coming sections, you will find them and make a good guess as to which one is better for your usage. With the help of these two tests, you can be sure that your tests will run smoothly, and the results will make your marketing much better inbound traffic than before.
With A/B testing, you learn a lot about how your website’s formatting affects sales and traffic. Or how a piece of content will perform in terms of engagement. The multivariate test will show you which of the elements present on your web page will positively impact your traffic or user engagement. It does this by showing you the multiple variations of the same elements along with the results of the tests. Apart from this, the A/B testing can only take in 2 variables of instances or elements. The Multivariate is going to have more than two variables for the tests.
Multivariate Testing Definition
The core mechanism of Multivariate testing is similar to that of A/B testing, but the added advantage of this form of testing is the addition of more variables. With more variables, the results show more information about how these variables interact with each other. A multivariate test is used to determine the effectiveness of the design combination of various variables to land on the ultimate goal. When performing multivariate tests, you get data for each variation to find the most successful design. Apart from that, it also shows you which of the element here has the most positive impact on your results and which one has the negative impact on visitor’s interactions.
Common Use Case Of Multivariate Testing
The most common use case of multivariate testing is when you are trying to find the best elements for your web page that gives your better user interaction statistics. Let’s take an example to find out the working of multivariate testing; suppose you have a web page where you have added a sign-up form, a catchy header text, and a footer. To run a multivariate test on this specific page, you can create two types of sign-up forms and three different types of titles for the web page; you can also use four types of footers for one test.
When the setup is done, you next need to create a funnel that will visit all possible combinations of the elements you want to test out. This form of testing is also known as factorial testing, which is one of the reasons why people tend to go with multivariate testing rather than A/B testing in the first place.
Multivariate testing is only recommended for those websites or applications that have a high amount of traffic daily. With the increase in the number of variations, you will be running tests for a longer time. Thus, the results will be more complex and meaningful. Once the testing phase is completed, the results for each variable and variation will be compared.
Besides this, their performance in the other versions’ context will also be explored. The results will give you a clear winner of which of the elements present on your page is going to give the best performance. In the above, the combination of the first sign-up form, with the first title of the web page, and the fourth type of footer makes the best combination to attract more views, clicks, and actions from the customers.
Different Types Of Multivariate Tests
There are two significant forms of multivariate tests that one can perform with ease, and both of them are explained down below:-
Full factorial:- In this method of multivariate testing, all the different combinations of variable elements will be tested out by sending equal amounts of traffic to each of them. For example, if you are testing two different variants of a single element and then three different variants for another element. Then in total, you have six elements along with the presence of three variants for others. Here each of the six combinations that you have will now be given 16.66% of total traffic to test out which is the best combination from all of them.
Fractional factorial:- Just from the name, you can figure out one thing, and that is in this form of multivariate testing, we are only targeting a fraction of all combinations and sending a limited amount of traffic on other combinations to find out the results. The conversion rate of the untested combinations will be deduced based on the statistics of the ones which are actually tested using the multivariate testing method. One of the issues with this testing method is that it has fewer combinations due to less traffic being sent to the combinations. The results are less precise than the first method, called fractional factorial.
Benefits Of Using Multivariate Testing
There are three significant benefits of multivariate testing, and those are:-
- First, you don’t have to worry about performing a number of A/B tests one after the other to find which of the solutions works best for you. As a result, you will be saving a lot of your time. With multivariate tests, you get to perform multiple A/B tests simultaneously on the same web page.
- As an owner or a developer of a website, you get to clearly see the contribution of each variable in the testing result, and it makes measuring the gains relatively easy as well.
- Lastly, with the help of multivariate testing, you get to measure the effects of interactions between several supposedly independent elements of your web page.
Limitations Of Multivariate Testing
The first limitation of multivariate testing is balancing the volume of visitors for each subject that you are testing in order to find out usable results. When we take in the number of variables along with possibilities and multiply them with one another, the combination of the two can be skyrocketing.
The samples we have assigned to one another can be reduced mechanically. When you are performing A/B testing, you are allocating 50% of the total traffic to the original element, and the remaining 50% is allocated to the variant element. This makes your job much easier. But with multivariate testing, you could be allocating 5%, 10%, 25%, or more depending on the type of combinations you are using with your multivariate test. On the other hand, this also results in a much longer testing time, and you are not getting the statistical reliability till the time all the tests are complete. Now, if you are someone who is testing your more profound and more content-filled pages, then it is hard to divert the traffic to those pages, and you will end up with low traffic results as well.
The second issue we need to highlight here is how multivariate testing is chosen to be used. In most cases, it might be due to the fact that you have accepted there is a weakness in your website deployment. The users don’t exactly know what needs to be done when they visit your website, and they test out multiple things on your website at once in order to find something to use in the end. Apart from this, you will need to add minor modifications to these tests to make them work.
Lastly, we have a significant concern of complexity; when it comes to A/B testing, things are pretty much more straightforward, and one can understand it clearly even without having any prior knowledge of what is happening in the test and what are the end goals of the test. There is no mental stress. You need to go to find out which one is the best element for your customers. There is a clear winner in A/B testing after the test has been concluded. One of the variants is going to perform better than the other. This keeps the whole process of testing out the elements simple and much faster in terms of execution. Moreover, you will be more confident in these results and quickly make changes on your site accordingly.
Definition Of A/B Testing
With the use of A/B testing, you want to find out the difference between the two specific designs against one. In addition to this, you are also looking to find out meaningful results in the shortest time possible. Likewise, if your website is new and you don’t have enough traffic on it, then A/B also becomes your preferred choice as there is no critical data needed for you to test out two variables. In this method of testing, the two versions of the page will be given live traffic, and the visitors are bucketed to one version or the other. Finding out how visitors are interacting with the page they are shown will give you the result of the test. The difference could be in the videos they are watching, the button they are clicking, and signing up for the newsletter forum as well. From the results, it will be clear which one of the page’s designs or elements is better than the other.
Common Use Case Of A/B Testing
As we said earlier, A/B testing is one of the most accessible tests one can perform to find the best version of their web page, application, and other elements as well. This testing method can come in quite handy in lots of situations, some of which we have discussed below.
First, the most common method in which A/B testing is used is to find out which design architecture is better. To find out the result, designers tend to compare or test both designs against each other. Let’s take an example to explain things more clearly. Let’s suppose you have an e-commerce website, and its product page might have a text-based call to action. But you have developed a new version where most of the text is eliminated, and the products are now being shown to customers according to their preferences dynamically. Here you will send visitors to both pages to find out which one is the better. Even after the change in design elements, it is still considered to be a form of A/B testing. The impact of the design is tracked by the test results as a whole and not by the individual elements.
Apart from this, the second common use of A/B testing is its use as an optimization option where you need to make changes in only a single element of the whole page. For example, suppose you own a website for kids learning, and you perform the A/B testing on the home page to find out which of the sign-up element yields the best results. In this case, you have one sign-up form which shows a picture of a mother and her kid. The second variant has a picture of a teacher and a kid in a sign-up form. The A/B testing here will give you precise results. In addition to this, you could even add more than two variants for the test, and it will become A/B/C/D tests. This also means you have to split the traffic of your website into three or four equal percentages as well.
Benefits Of Using A/B Testing
One of the most significant advantages of A/B testing is that it is simple yet powerful. This makes A/B testing the most widely used method. Likewise, the tracking numbers are pretty small, which shows you get to see reliable data in a short time. The other important aspect of A/B testing is that it does not require a high amount of web traffic to give you accurate results. This makes it especially helpful when a site is newly built and has a small number of daily visitors that could be used for testing.
A/B testing is so accurate and fast that big companies like Facebook, Instagram, and WhatsApp are all using it as their primary method for testing, running various cycles of tests one after the other.
A/B testing also provides valuable insight into optimization, and you can implement it on skeptical teams and see the results of their designs, code, and other forms of work deployment. The results will show the quantifiable impact of their work and clearly shows which one is the winner.
Limitations Of Using A/B Testing
A/B testing is considered to be the most versatile testing method, and when we pair it with the smart experiment design along with the presence of iterative cycles, it is going to provide you with the best results to showcase where your website needs to make an improvement. But the main limitation of this testing method can be found in the name.
With A/B testing, you are only able to measure the impact of two to four variants. That’s it. If you want to go above four variants, you need to switch to multivariate testing. The tests with more variants are going much slower, and you have to manually find out the necessary information of different variants as they are not present on a single page.
Wrapping Up
In truth, multivariate testing looks like a better option on paper. It is not a piece of cake to pull out in the first place. You will end up carrying out a test for a longer time, and that too will provide you with weak statistical reliability. As a result, when it comes to finding the best method for testing out a new update or a patch, companies prefer using A/B testing. In more than 90% of the cases, it is better to go with A/B testing. The remaining 10% can find out where they need to fine-tune the A/B testing and then start their testing. Even after doing so, they are not able to come to a conclusion; only then going with the multivariate testing should be considered as the last resort.
One more thing we need to talk about here is that, in the end, it is not the method of testing that is going to yield great results. It is the precision of your hypothesis and your understanding of the problem that you want to test out. The tests will only perform according to the level of precision and the depth of information you are able to provide for the testing of the activity.
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Simran works as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her.
