At first glance, A/B and multivariate testing sound similar. So, if you have been experiencing difficulty distinguishing between the two terms, it is perfectly justified.
After all, both involve testing differing page variations simultaneously to find the best-performing version that maximizes conversion rate optimization (CRO) and enhances the user experience. Nevertheless, they are distinctly unique in their application.
Knowing what testing method to use is critical for obtaining accurate and data-driven results for your business. In this brief read, you will be enlightened with a general overview of A/B testing vs multivariate testing, pros and cons, their core differences, and applicability.
Toward the end, you’ll be able to effectively figure out when and where to apply these testing methods.
A/B Testing vs Multivariate Testing: General Overview
Before delving into the core differences, let us analyze A/B testing and multivariate testing from the surface level. How do you define the two? What are the advantages and limitations associated with each method? Let’s find out together.
Explaining A/B Testing
A/B testing, also referred to as split testing, is an experimentation method where two webpage variations, A and B, are carefully compared to see which version drives greater traffic, click-through rate, and conversions.
According to 99Firms, 77% of businesses are utilizing A/B testing to improve the designs of their webpages. This simple and swift experimentation method is helping them generate greater revenue, improve user experience, optimize dynamic landing pages, and reduce the chances of sales abandonment cart.
For example, a company wants to test two price points for a product. In version A, the product is priced at $15, whereas the B variation carries the price tag of $18.
The data collected with A/B testing would help determine the ideal price point that increases revenue and resonates with the company’s target audience.
A/B testing doesn’t just reside with webpages; there’s A/B testing for pricing, emails, landing pages, and more. It’s important to note that the page variations are broadly different from one another in A/B testing.
A/B Testing: Pros and Cons
A/B testing is a remarkable experimental method to evaluate what’s working for you and what’s gathering dust. Naturally, it has its strong and weak spots.
Here are the top benefits of incorporating A/B testing in your company:
- Making data-driven decisions: A/B testing offers concrete data to support decisions, reducing reliance on guesswork and personal opinions
- Boosting conversion rates: By strategically segmenting traffic, trying out a few variations can help pinpoint which variation generates the most conversions.
- Super fast: Due to its simplicity, for most companies, A/B testing is the primary method for optimizing their pages
- Minimizing risk: A/B testing empowers businesses to implement small, gradual changes instead of large, sweeping ones, thereby reducing the risk of negative impacts as only one variable is tested at a time
- Improving user experience: Experimenting with various elements such as headlines, images, or calls to action helps identify what resonates most with users, resulting in a more captivating and user-friendly experience.
- Easy to use: A/B testing is not complex, as you usually only deal with 2 to 4 variations; thereby, results are easier to interpret.
Common limitations include:
- Limited variations: A/B testing is conducted to monitor big changes only, so if you are looking to test out complex elements within a single page, then it becomes ineffective
- Sample size: A/B testing requires a sufficient sample size to segment your audience successfully and without which the statistical significance is diminished, leading to inaccurate findings
Explaining Multivariate Testing
In simple words, multivariate testing works just like A/B testing. However, it holds more complexity since it requires creating multiple page variations with unique element combinations. These elements often include headlines, CTA buttons, minor design layouts, website copy, and more.
For example, a company looking to experiment with 2 hero images and 2 CTA buttons would have to create 4 page variations. HI1/CTA1, HI1/CTA2, HI2/CTA1, HI2/CTA2. Multivariate testing will help determine the efficacy of each of these element combinations.
The data collected at the end would aid in selecting the best-performing page variation combination. Multivariate testing is usually put into action after A/B testing to further tune webpages.
A study, published in Smashing Magazine, showed that multivariate testing was conducted on multiple page elements to enhance software downloads. To their surprise, multivariate came through with a whopping 63.2% increase in the conversion rate.
Multivariate Testing: Pros and Cons
As you already might know by now, multivariate testing proves to be an invaluable tool when you need to optimize or personalize page variations with great attention to detail.
Here are the top benefits of incorporating multivariate testing in your company:
- Versatility: There are more page combinations to work with
- Audience segmenting: Multivariate testing allows you to segment a percentage of your target audience for every page
- Increased engagement: successful multivariate testing paves the way for enriched personalized user experience.
Common limitations include:
- Time-consuming: multivariate testing, in contrast to A/B testing, needs more time to set up and run
- Complicated results: Because of the large number of page variations, tracking and monitoring results for multivariate testing can be difficult.
- Large sample size: If you are segmenting a percentage of your audience for each variation, then the overall sample size needs to be large enough to drive accurate data from all fronts.
A/B Testing vs Multivariate Testing: Core Differences
It’s time to break down the key differences between the two testing methods. As aforementioned, A/B testing and multivariate testing are distinctly unique in their applicability and purpose. How? Let’s find out in detail.
Testing Traffic Volume
In contrast to A/B testing, multivariate testing requires a much larger traffic volume. This is to segment a healthy percentage of the target audience for every variation combination.
For example, where a traffic volume of 500 people might suffice for A/B testing’s two-page variations, it’ll fall short for multivariate testing, where it needs to be divided among 5 or more variations.
Number of Variations
A/B testing, as the name suggests, has two variations (A and B). Multivariate testing involves evaluating specific elements within a page. Anything from an image, headline, piece of text, and layout adjustment.
For this reason, multivariate testing kicks it up a notch and includes a higher number of variations in comparison to the A/B testing method.
Major vs Minor Tweaks
In A/B testing, the two variations are very different from each other. For instance, two distinct color palettes for page A and page B. They are easily distinguishable at first glance.
In multivariate testing, minor changes are implemented to evaluate the performance of a multitude of elements on a page.
Main Objective
A/B testing is conducted to finalize the best-performing page. Multivariate testing, on the other hand, is carried out to locate the most efficient working elements on a page.
Time Window
Because there are fewer distinctly different variations, A/B testing is faster to conduct and takes less time to collect data.
Multivariate testing; however, requires a bigger time window to accurately monitor the performance levels of each element within a multitude of page variations.
Application of A/B Testing
When should you utilize A/B testing? Well, it’s quite straightforward!
- When you are dealing with a single variable
- Testing up to 2-page variations
- The focus is solely on big changes
- You want to collect data faster
- Your average traffic volume is 500 or higher.
Application of Multivariate Testing
Multivariate testing is usually needed at a higher stage as it involves diving into the specifics of a webpage. Here are the criteria for conducting multivariate testing:
- You want to test numerous element combinations within a page
- You want to test a higher number of page variations
- The focus is on smaller changes
- You want to decipher which combination lands better impact
- You have already completed A/B testing and want to tune the page further
- You have an average traffic volume of thousands
Conclusion
It is vital to comprehend the differences between A/B testing and multivariate testing to optimize conversion rates effectively. A/B testing involves comparing two different variations, making it suitable for significant changes and faster outcomes.
Conversely, multivariate testing delves deeper by evaluating multiple elements at the same time to gain detailed insights, although it necessitates larger sample sizes and more time.
The choice of the appropriate method relies on your testing objectives, audience size, and the complexity of the changes you intend to introduce.
Looking for a trusted A/B testing tool that would convert your visitors in minutes? Relevic is a highly optimized, non-technical, and user-friendly tool that can aid in making your webpages reach their true potential. Happy testing!
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