Multivariate testing is a method that allows you to perform a more detailed and subtle test to optimize the conversion rate. It gives you insights about what elements on a page are the most important for achieving your goals with that page.
Moreover, multivariate testing shows you how different elements on a page interact. The final versions about to be tested are combinations of these elements.
So the particularity of a Multivariate test is that it reveals how effective are specific combinations of elements on a web page.
Key Takeaways
- Multivariate testing identifies optimal combinations of multiple-page elements for conversions.
- A/B testing evaluates one variable at a time, while multivariate testing assesses several.
- Multivariate tests require more traffic due to the complexity of testing multiple combinations.
- The main benefit of multivariate testing is detailed insight into effective page designs.
- A challenge with multivariate testing is the difficulty in pinpointing which variable impacts outcomes.
Conducting A/B and Multivariate Tests
Both A/B tests and multivariate tests follow a similar process, with the primary difference being that A/B tests evaluate one variable or the overall page at a time, while multivariate tests examine multiple variables simultaneously.
A/B Split Test
In an A/B split test, two versions of a web page (or email, ad, etc.) are presented to users, with traffic split evenly between them to determine which version is more effective. The winning version can then be tested against other variations in subsequent tests.
For example
- Test 1: Compare the current control page with Page A. If Page A performs better, it becomes the new control for the next test.
- Test 2: Test Page A against Page B. If Page A still performs better, it remains the control.
- Test 3: Test Page A against Page C. If Page A continues to outperform, it is confirmed as the optimal version of the page.
A/B/n Testing
An alternative approach is A/B/n testing, where multiple versions of the page are tested simultaneously, with traffic evenly distributed among them.
For example
- Control Page: Receives 25% of traffic
- Page A: Receives 25% of traffic
- Page B: Receives 25% of traffic
- Page C: Receives 25% of traffic
Multivariate Test
Before conducting a multivariate test, identify your key performance indicators (KPIs) and the page elements likely to influence them. Decide on the variations of these elements to test. Multivariate testing involves comparing a larger number of page versions than A/B testing, as each combination of variants must be evaluated.
For example
- Combining 3 Headlines, 2 CTAs, and 2 Images: Results in 12 different page versions to test.
- Traffic Distribution: Evenly split traffic among all page versions to ensure reliable results.
- Example Page Combinations: Headline A + CTA A + Image A, Headline A + CTA B + Image A, etc.
Due to the increased number of combinations, multivariate tests require a higher volume of traffic compared to A/B tests. It’s not uncommon for multivariate tests to involve 8-25 different combinations that need to be evaluated.
Recent Insights
According to a 2023 report by ConversionXL, companies that adopt a structured approach to A/B testing see an average improvement in conversion rates of 30% (Source: ConversionXL, 2023). In a recent case study by Optimizely, an e-commerce company achieved a 15% increase in sales by optimizing their checkout page through multivariate testing (Source: Optimizely Blog, 2023).
Best Practices for Implementing Multivariate Testing
To ensure the success of your multivariate testing efforts, follow these best practices:
- Start with a Clear Objective: Define what you aim to achieve with your multivariate test, whether it’s increasing conversions, improving engagement, or enhancing user experience.
- Select Relevant Variables: Choose elements that are likely to have a significant impact on your objective. Common variables include headlines, images, call-to-action buttons, and form fields.
- Create Hypotheses: Based on your objective and selected variables, formulate hypotheses for each combination. For example, “Changing the headline to X and the CTA button to Y will increase conversion rates.”
- Use a Robust Testing Tool: Select a reliable multivariate testing tool that allows you to set up, monitor, and analyze your tests effectively.
- Segment Your Audience: If applicable, segment your audience to ensure that you’re targeting the right users with your test. This can help you gain more accurate insights.
- Test Simultaneously: Run all variations of your test simultaneously to ensure that external factors affect each variation equally.
- Analyze Results Thoroughly: Once the test is complete, analyze the results to determine which combination performed the best. Look beyond the primary metric and consider other factors that may have influenced the outcome.
- Implement and Iterate: Implement the winning combination on your website and continue to iterate with further tests to optimize your results continuously.
- Document and Share Learnings: Document the results and insights gained from your multivariate test. Share these learnings with your team to inform future testing and optimization efforts.
By following these best practices, you can maximize the effectiveness of your multivariate testing and make data-driven decisions to improve your website’s performance.
A/B Testing vs. Multivariate Testing
A/B Testing is a method of website optimization in which you compare two versions of a page (version A and version B) using live traffic.
When you perform an A/B Testing experiment, you split the traffic (most of the time it’s 50/50 split) between each page.
Multivariate testing is a method that allows you to make changes to multiple elements on a webpage.
The main purpose of an MVT test is to give you the big picture of which elements on a specific web page perform better and play an important role in increasing the conversion rate and achieving the objectives of that page.
In other words, a Multivariate test reveals more information about the design combination that is more effective on a certain page and how the involved variables interact.
MVT Disadvantages
Multivariate testing comes with one disadvantage: modifying too many variables at once in a single test won’t tell you which of the variables has a positive impact on KPIs.
As we said before, it gives you a macro perspective and does not analyze details.
Therefore, you assume the risk of seeing a drop in conversions after you implement the winning version.
Also, since a multivariate test involves many possible combinations, the high amount of traffic needed for the experiments to be relevant is a big downside for MVT.
Compared to an A/B Test, where most of the time the traffic is split in two, in an MVT you can split the traffic in 3,4,5, etc. variations, depending on the number of combinations tested.
MVT Advantages
The main advantage of an MVT is that it allows to test different layouts and identify the design that makes visitors convert more. Once you know the winning layout, you can start A/B Testing elements from it and improve the pages on site.
A/B Testing Disadvantages
In an A/B Testing experiment, there’s a limited number of tested elements.
In a single A/B Test, you can only change one variation at a time and it might take a while to test all the elements you want on a web page.
A/B Testing Advantages
A/B Testing reveals the impact of a change in the website’s design or copywriting on key performance indicators such as conversion rate or revenue.
A/B Testing is easy to implement and track.
The A/B Testing feature from Omniconvert requires no programming skills and you can track the results with a web analytics tool.
Unlike A/B testing, MVT is a more difficult method to implement.
Because of the larger number of combinations that can result in being tested, you will need a lot of traffic on your website if you choose to perform a multivariate test.
A/B testing is recommended for websites with a smaller traffic size, whereas Multivariate Testing works better on websites with a large amount of traffic.
Image credit: Search Engine Land
As far as you, as a marketer or website owner, are concerned, statistics show that multivariate testing is considered highly valuable by most companies that are focused on optimizing the conversion rate.
A study of Econsultancy and Red Eye showed that, in 2011, Multivariate testing was considered more valuable than usability testing and customer journey analysis.
However, not so many companies use multivariate testing as a method to improve conversion rate on their websites.
When respondents were asked what methods they intended to use to optimize the conversion rate, they placed multivariate testing below methods like copy optimization, usability testing, or cart abandonment analysis.
Statistics for what methods do companies intend to use for optimizing the conversion rate:
Statistics: the most valuable methods considered by companies for conversion rate optimization.
So, if you want to make multivariate testing work for your conversion rate optimization strategy
- Make sure that you have enough traffic on your website: a large number of versions tested needs a proper number of users;
- You will test subtle changes on your website, you will not make a complete change to its design; don’t forget that the role of MVT is to get more detailed information concerning your users/customers’ behavior;
- Run a test that makes a difference; use multivariate testing on the most important pages on your website; e.g.: a landing page where you test the length of a form combined with different shapes or copy for headers and footers.
Besides the two most valuable methods of testing for Conversion Rate Optimisation, you can find valuable information about your customers through surveys.
Convince them to not leave your website before filling out a form with data like email, company, or other characteristics. Combine these features to fabulously grow your conversion rate.
Frequently Asked Questions
What is the difference between a multivariate test and an A/B test?
The main difference between a multivariate test and an A/B test is the number of variables tested simultaneously. An A/B test compares two versions of a single variable, such as two different headlines or call-to-action buttons, to determine which one performs better. In contrast, a multivariate test evaluates multiple variables and their combinations at the same time, such as testing different headlines, images, and button colors all within the same experiment to identify the most effective combination.
What is multivariate testing in SEO?
In the context of SEO, multivariate testing involves testing multiple elements on a webpage to determine which combination of changes has the most positive impact on search engine rankings and organic traffic. For example, you might test different title tags, meta descriptions, and header tags simultaneously to see which combination leads to higher rankings for targeted keywords.
What is the formula for multivariate testing?
The formula for calculating the total number of variations in a multivariate test is the product of the number of variations for each element being tested. For example, if you are testing 3 different headlines and 2 different images, the total number of variations would be 3 (headlines) x 2 (images) = 6 variations.
What do multivariate tests show?
Multivariate tests show the impact of different combinations of variables on a specific outcome, such as conversion rate, click-through rate, or engagement. By analyzing the results, you can determine which combination of elements performs the best and understand how different variables interact with each other. This helps in optimizing the webpage or marketing material for better performance.
What is the main purpose of multivariate analysis?
The main purpose of multivariate analysis is to understand and interpret the complex relationships between multiple variables in a dataset. It is used to analyze patterns, trends, and differences in the data, identify key factors that influence outcomes, and make predictions based on those factors. Multivariate analysis is widely used in various fields such as statistics, finance, marketing, and social sciences to draw meaningful insights from multidimensional data.
