Customer Insights Analysis – What is it and its Strategic Uses?
Digital Marketing 11 min read
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Home Blog Digital MarketingMultivariate Testing vs. A/B Testing: A Digital Marketer’s Guide
Create marketing experiments that’ll help you hit your brand’s goals when you learn the differences between multivariate testing vs. A/B testing this year.
Your success online hinges on your capability to innovate your digital marketing. This can be done through marketing experimentation. Whether you’re running experiments to improve your paid advertising, email marketing, or overall user experience (UX), you need to know the basics of experimentation to be able to optimize campaigns and drive digital wins for your business today.
A/B analysis and multivariate testing are some of the most basic, yet most essential methods of marketing experimentation in the biz. In order to use these testing methods in effective ways, you need to understand their definitions, use cases, and differences, so that you can decide which one will work best for the goals you have in mind for your business’ success.
Excited to utilize these two methods to improve your marketing and drive digital wins for your brand? Then keep reading this guide by Propelrr to discover how to use multivariate testing vs. A/B testing in email marketing, paid advertising, UX, and more today.
At its core, A/B testing is a type of test that compares two versions of an ad, landing page, website, or email, to see which version performs the best. Also known as split testing, this technique allows you to optimize your digital marketing executions and improve overall performance online.
The pros to this method of experimentation include:
The cons of this method, on the other hand, include:
Given these pros and cons, you might be interested to know the specific cases where it’s best to use this type of experimentation for your marketing needs. Below you’ll find some scenarios and examples of when to utilize A/B analysis to improve your chosen campaigns.
Here are some use case scenarios for using split testing in your optimization journey:
Here are two successful case studies that showcase the appropriate use of this testing method:
Think you have a handle on A/B testing when it comes to your digital marketing campaigns? Then it’s time for you to learn more about multivariate testing, to see if it’s the right fit for your experimentation today.
Multivariate testing (MVT) is a method that lets you analyze multiple variants of an ad, landing page, website, UX, or other marketing execution, to see what combination of variables works best for said execution. Since you can test more versions simultaneously with this type, you get results that are more complex than what you’d get from a traditional A/B analysis.
Given that definition, the subsequent pros of this method of experimentation include:
The cons of MVT, on the other hand, include:
There are some unique use case implications for MVT, in light of the pros and cons listed above. Discover the situations and scenarios in which you can use this form of analysis by checking out the lists below.
Here are examples of scenarios where you can use MVT to optimize your marketing execution:
Meanwhile, here are examples of case studies where MVT was utilized and executed correctly:
Given all these excellent definitions and examples of the A/B and MVT methods, you’re now better equipped to understand which method might work best for your digital marketing needs today. Let’s keep bolstering your knowledge of these two types by comparing them against one another in the next section.
Each test is useful in its own way, and one may not be a great substitute for the other due to some key differences. As you compare each test type for your optimization goals, remember to factor in the following unique differences between the two methods too:
A/B Testing: | Multivariate Testing: | |
---|---|---|
Methodology and research design | Compares two variations on a single variable for an ad, landing page, UX, or other marketing execution | Compares multiple variables in multiple variations for an ad, landing page, website, UX, or other marketing execution |
Statistical significance and data interpretation | Smaller audience pool may imply higher risk of false positives, leading to the necessity of more A/B tests to collect more data | Necessity for a larger audience pool results in more data points collected, implying lower risk of false positives |
Resource and time requirements | Longer amount of time for sequential experiments, fewer resources like budget and manpower due to simpler execution | Shorter amount of time due to multiple comparisons in one run, more resources like automated tools, website traffic, and analytics needed |
The best method of choice will inevitably depend on the optimization needs of your selected marketing campaign. But aside from the test’s suitability for your needs, you should also see what tools you have at your disposal to run these experiments overall.
Follow us into the next section to discover four essential tools and platforms for running an A/B analysis or MVT this year.
The decision-making process for picking between these two types should also include tools, platforms, and technologies available to you when running your experiment. If you don’t have the tools you need to run a multivariate analysis, for example, then you might need to restrategize and do an A/B comparison instead.
Here are some examples of essential testing tools and platforms for setting up your experiments, tracking their progress, and collecting data for your expert interpretation:
With this short list of heavy-hitting software for marketing experimentation and optimization, you can set up a solid starting point for the improvement of your campaigns and content from this point forward.
No matter which method you choose, the important thing to remember is that you should always experiment with your content. Testing out your campaigns is key in hitting business goals; without it, you won’t be able to innovate your executions in successful and data-driven ways.
Testing and experimentation empower data-driven innovation in digital marketing. With them, you can address critical pain points, discover data-backed solutions, and drive campaigns that return real results for your brand in the long run.
Drive innovation with the right types of testing today. Here are a few final reminders to take with you as you embark on your digital marketing journey today:
If you have any other questions, send us a message via our Facebook, X, and LinkedIn accounts. Let’s chat!
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