Understanding your customers' needs is the topmost priority for any online business. Thus, testing helps you to get valuable insights into your customers, such as what they are looking for, what they want from your website, how changing an element can impact their behavior, etc. using these facts and figures about the customers, you can work on optimizing your website as per their needs to improve the conversion rate. For testing your website, you can use any testing like A/B testing, multivariate testing, split testing, and others as per your project requirement.
A/B testing is a commonly used test, but it is not helpful in some scenarios. Thus, you can consider multivariate testing for advanced testing needs. Consider your website's elements that decide the user experience. But choosing the right combination of these elements is essential as they are the driving factors for generating leads. This is where multivariate testing comes into play to find the right combination for you that will work to optimize your website.
Today, most marketers are adopting multivariate testing for more optimum results. But for other marketers, it is still a question of what multivariate testing is, how it is implemented, its benefits, limitations, and how it is different from A/B testing.
We will be focusing on multivariate testing, the process of implementing it, its pros and cons, best practices, and other necessary details.
The high-end competition of creating advanced websites that stay a step ahead of others gives rise to the need for multivariate testing. It is preferred to fulfill the advanced testing needs of the marketers where they do not want to miss any chance of losing conversion rates and leads.
Multivariate testing refers to the testing practice where it tests various website elements. The term "multi" within the multivariate specifies the combination of elements or several elements that must be tested simultaneously to optimize the website. Sometimes changing a single element will not impact the conversion rates, but finding the right combination will boost the performance.
For example, you want to test the new image, video, modified content that goes well with the new marketing campaign. For that, you have to test several combinations, and multivariate makes your testing process more straightforward and efficient to get improved results.
To make the test process more manageable, you can use any multivariate testing tools to run tests with real-time results using the same audience. The different combinations will be accessible to the audience to analyze their behavior at different times. The combination that gets more traction is the winning variation of the original website.
You might not find it interesting if you have worked with A/B testing. But in some cases, you will not get the insightful results with A/B testing that you might get with multivariate. For that, you get into the detailed difference between A/B testing and multivariate testing.
Both testings work well for different scenarios and help to optimize the website's performance to generate more leads. The most basic difference lies in their implementation and generating results. We will discuss each factor that differentiates both of them.
In multivariate testing, we look for the right combinations of the website's elements that complement each other and effectively attract customers. While in A/B testing, we test a single element to see the customer's reaction to the specific element. The purposes are different. Some consider multivariate to be complex, but it is not.
With A/B testing, you can create two or three variations of the website. Still, while running multivariate testing, you can check dozens of combinations via testing several web pages and make them available to the users at different time.
In the case of A/B and multivariate testing, the entire traffic is evenly divided among the number of versions created of the website at the same time to analyze the results. It means that you will require more traffic than A/B testing for conducting multivariate testing.
With A/B testing, you can get the best page, while with multivariate testing, you can get the best combinations of elements on that page. It is why the A/B testing ensures the global optimum of the website, and multivariate testing ensures the local optimum of the website.
Also, A/B testing is essential for making significant changes to the website with fewer versions, and multivariate testing is essential for making small but several changes to the website.
Analyzing the results of A/B testing is more accessible as the variants have completely different changes; thus, it is easier to decide the winning variant. But, in the case of multivariate testing, you need to analyze several variants to check which combination is more effective. Sometimes more than one combination can show the same results, and then it is upto you which one to choose.
A/B testing takes less time than multivariate testing, as in multivariate, you need to analyze several variants.
We have different approaches for implementing multivariate testing as per different scenarios. Below are the three different approaches to the multivariate testing process.
This type of multivariate testing uses more than two factors, each with unique levels. The testing will consider all possible combinations for each level across all factors. The entire traffic will then be divided equally among all the combinations. Such type of testing is possible when you have tons of traffic. You will get more statistical results.
This type of multivariate testing allows you to carefully and smartly choose the subset of the full factorial testing test runs. It considers only the sample set by using the most significant combinations. In comparison to full factorial, this testing will require less traffic.
Adaptive multivariate testing is a new approach that helps analyze real-time insights about how the visitors respond to the website and find the best version providing optimum results. It helps you find the best possible combination to improve sales.
Using A/B testing, you can only change one or two elements at a time, but what if you want to see the effect of some combinations within a page. At that time, A/B testing cannot help you check for several combinations. For that, you need to implement multivariate testing to determine the impact. This will help you make more concrete decisions, as sometimes changing the combination of more than one element can do wonders.
Here are some benefits of implementing multivariate testing.
With multivariate testing, you can analyze the changing behavior of the customers. You'll get the stats regarding how they behaved to different combinations changes on the webpage. The more effective the combinations, the higher conversion rates will be. You'll also re-orient the customers with the website.
Using the data from the multivariate testing, you can easily apply it to future campaigns. After testing, you will get the idea of which combination worked well, and you can design the landing pages keeping all those factors and combinations in mind for better results. Next time, you will take less time to get to the winning webpage to boost the website's performance.
Sometimes customers get lost within your website as they do not get what they want and bounce back immediately. You can use multivariate testing to restructure your website's elements to resolve this issue. You will understand where to place which element on the webpage. Once you place the elements properly, you will see the difference in the conversion rates.
Multivariate testing allows you to choose from a wide range of elements to make the changes and find which combination works well in attracting potential customers. It will increase the testing options to get the most desired outlook for the website.
Despite many benefits, you will face some limitations while conducting multivariate testing. We have mentioned some of the common limitations you should consider while conducting multivariate tests.
The first limitation is the number of visitors or traffic coming to your website to get valuable results. Once you multiply the number of variables and possibilities to be tested, you will get a huge list of combinations. The traffic will get divided automatically among all the combinations. In the case of the A/B test, you divide 50% of your entire traffic to the original and its variant, but in the case of multivariate, you can only use a few percent of your traffic assigned to each combination. Thus, you require massive traffic for running multivariate tests. Low traffic sometimes results in more extended tests and an inability to get enough data for concrete decision-making.
Without knowing what combination to test, it isn't easy to start. In some cases, users cannot predict which elements to test. Sometimes, they choose random or several elements at once, thinking they will find something to use. People usually find small changes at work in these tests. While A/B testing, on the other hand, allows better identification of test hypotheses, leading to more creative tests supported by factual data and providing better results.
Another limitation is related to complexity. Running an A/B test is much simpler, as it provides valuable insights into the customers. With A/B testing, you do not have to perform complex guesses to understand which element will do the magic. You must do the calculus with multivariate numbers to find which combination to check. Only then can you start the testing.
Below is the process to conduct multivariate testing; this process might vary from project to project.
Before you think of running tests, make sure you go through your data and find out how customers behave around your website, what they like the most, and what they dislike. For example, if you see that visitors are not opting for the "download" button on your website. How can you run the multivariate test to influence visitors to go for the download button?
After examining, the team noticed that the download button was going unnoticeable. So, the team agreed on improving or restructuring the page. The possible solution is to make the button more noticeable so visitors will go for it. The team tried to change the font, color, and size to complement the entire page without impacting the user experience.
The hypothesis is to make the download button more engaging.
The team works on the hypothesis by creating variants with different and effective changes. It is important that the team works on two factors on the page, such as making some changes to the "Download" heading in the sidebar and creating the "PDFProducer" download link below it.
The main goal of this change was to observe the combined effect of the word "free" and highlight the download section.
For the "Download" link, the team has tested three different variations:
"Download" in red
"Download for Free" in red
"Download" in default color, but a larger font size
For the original "PDFProducer" link,
"PDFProducer" in default color, but a larger font size
"PDFProducer" in red
Below are the possible combinations.
Total, 12 different variations were formed, perfect for running a full factorial test.
The next step is to determine the sample size means the minimum number of traffic required by each variation to get some valuable conclusion. You can divide the traffic depending on the number of variations and the entire traffic. Some tools will help you figure out how many visitors you need to maintain and how long the test should be run.
Before running traffic, make sure that you have considered some factors, such as previewing your landing page in every browser, working of the CTA button, the accuracy of all URLs and links included, etc.
Ensure to QA every aspect of your campaign to eliminate the chances of failures.
After creating variations and understanding how much traffic you need to generate, you can now drive the traffic to each variation. The only problem with multivariate testing is that you will require a massive amount of traffic to be divided to get to an appropriate conclusion.
It is essential to analyze your results to choose one productive combination. Among 12 combinations, choose the right one to get more attention and generate leads. You can use various tools to get insights into the customer's behavior and react to the changes. Now, you can finalize the winning variant.
Once you gather insights and understand which combination worked well, you can use those data to design the website in the future. You can test the customer's behavior and react to the specific combination and make sure to use it next time.
To avoid mistakes while conducting multivariate testing, you must ensure the best practices. Below are some best practices that you might consider.
Multivariate testing is very technical, so make sure that you choose the right testing tool that makes every step easier for you.
Testing consists of various tasks that need to be conducted by each member of the team. So always hire experts and skilled members such as data scientists, developers, testers, designers, etc., so have a cross-skilled and enthusiastic team member to keep the work on track.
Multivariate testing is all about planning. Before running the tests, define what elements you want to test and how long it will take, and gather meaningful insights.
Set some goals and track your success consistently to get updates. You can use the right tools to achieve your goals.
Create a knowledge base based on the learning you will get from the earlier tests to use in the future. It helps the team members to be more efficient and productive.
Irrespective of your business size, multivariate testing can do wonders for your website. It will determine which combination works well and gets more traction than other possible combinations. The best way to implement multivariate is to conduct it after the A/B testing for optimizing your website. You can use multivariate testing to make small changes using various variants and divide the traffic among all the variations. Analyzing the testing results helps you gather valuable insights into the customer's behavior.
It takes much traffic to conduct multivariate testing, so make up your mind if you want to continue with it. Once you are successful in running this test, you can see how it boosts the productivity of your website.