Multivariate Testing

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multivariate testing

Did you know? Researchers used multivariate testing in the 1930s to optimize magazine and newspaper ads, sending different versions to different regions to see which performed best.

Nowadays, software companies experience a 27% increase in conversion with multivariate testing, as per a case study by CrazyEgg

So, don’t underestimate the power of small changes, it can significantly increase your conversions. 

Let’s get into the details of how multivariate testing can affect your business. 

What is MVT (Multivariate Testing)?

Multivariate testing, also known as MVT is a powerful technique for optimizing websites, apps, and other digital experiences. MVT could be used in design, customer journeys, marketing funnels etc

Imagine a landing page with a headline, image, and call-to-action button. You might create different versions or variations of each element. Multivariate testing determines which combination works the best. 

Let’s say you have created 3 variations of images, and 2 variations of call-to-action buttons, then you would have 6 variations of your website to test.

The total number of variations in multivariate testing will always be 

[No.of variation for 1st element] x [No.of variation for 2nd element] x … = [Total no. of variation]

How is multivariate testing different from A/B testing?

Number of Variables

A/B testing focuses on one variable at a time. It compares two versions of that variable, like two different headlines, a call to action button, etc.

Multivariate testing focuses on multiple variables simultaneously. It involves multiple combinations of different variations like headlines, images, call to action button, etc

Type of Insights

A/B testing shows a single version of a specific variable that performs the best but doesn’t provide insights into how variables interact.

Multivariate testing shows the interaction effects and how different variables work together. This allows you to identify not just the best individual elements, but also the most effective combinations.


A/B testing is relatively simple to set up and analyze.

Multivariate testing can be more complex due to the number of variables and combinations. It requires careful planning and statistical analysis.

Choosing between A/B testing and MVT depends on your specific goals and resources.

What are the benefits of Multivariate Testing?

Multivariate testing (MVT) offers several compelling benefits over traditional A/B testing, making it a powerful tool for optimizing digital experiences. Here are some key advantages.

Comprehensive interactions

Multivariate testing goes beyond comparing different versions of individual elements. It reveals how different elements influence each other’s performance. It also provides a better understanding of user behavior. 

Identifies optimal combinations

Multivariate testing doesn’t just cover a single element, it shows the most effective combination of elements working together to maximize your desired outcome. 


Multivariate testing allows you to refine your website much more precisely than A/B testing. You can tweak individual elements within the best combination to further optimize your website performance.

Maximizing results

When you focus on the best combination of the elements, multivariate testing can provide greater improvements to your key metrics as compared to single-variable A/B testing. 

Faster ROI

Multivariate testing can provide you with deeper insights and more targeted optimization, which in turn can deliver faster returns on optimization efforts. 

Reduced Risk

Multivariate testing reduces the risk of implementing changes that might not work, as you would get a detailed analysis of the report on which variations work the best. 

How to start multivariate testing?

Here is a step-by-step guide on how you can start with multivariate testing of your website.

Step 1: Identify the problem and define the goal

Start by pinpointing a specific area of your website, app, or business process that needs improvement, such as a low conversion rate on a landing page or a high bounce rate on a product page. 

Then clearly define measurable goals for your test, such as increasing conversions by a certain percentage or reducing bounce rate by a specific amount.

Step 2: Formulate a hypothesis

You can try to develop a hypothesis about why you think the problem is occurring and which elements might be influencing user behavior.

For example, you might hypothesize that certain combinations of headlines, images, and calls-to-action will lead to better conversion rates.

Step 3: Choose variables to test

Select the specific elements you want to experiment with.

Common variables include:

  • Headlines
  • Images
  • Call-to-action buttons
  • Form fields
  • Layout and design elements
  • Product descriptions
  • Pricing options

Step 4: Create variations

For each element, create multiple variations that you want to test.

Design these variations with careful consideration of user experience and best practices.

Step 5: Setup your multivariate testing

You should use a dedicated MVT tool or platform to create the test variations and implement them on your website, app, or business process.

Define the duration of the test. Make sure to give enough time to collect significant data.

Step 6: Launch the test and monitor results

Once you have set the test live, track its progress and gather data on how different combinations are performing.

Use statistical analysis to determine which combination is the most effective in achieving your goals.

Step 7: Analyze and implement changes

Analyze the test results to identify the best performing combination of elements. Implement those combination on your website to improve it’s performance. 

Tip to keep in mind while performing the multivariate testing.

  • Begin with a few variables to manage complexity and gain experience
  • Ensure sufficient traffic to obtain meaningful results as it may require more time than A/B testing.
  • Use a dedicated tool for the testing.

What are the limitations of multivariate testing?

While multivariate testing (MVT) offers meaningful and useful insights, it has also got some drawbacks.


While doing multivariate testing, the number of variables and combinations increases as compared to A/B testing. So it requires careful planning, setup, and statistical analysis. 

If the analysis is complex you might need an expert to interpret the accurate results. 

Traffic Requirements

To achieve statistically significant results, MVT often requires a substantial amount of website traffic. But with more number of combinations, it gets difficult to divide the traffic. 

The amount of traffic to each combination receives significantly decreases. Unlike in A/B testing where you can divide the traffic 50% for both variations.  

Test Duration

Due to the increased number of variations you have to give longer time to each combination so as to collect a significant amount of data. 

Interpretation Challenges

Understanding how multiple variables interact can be challenging, especially when there are so many variables involved. 

With such a complex interpretation, there are chances of misinterpreting data and identifying false positives. 

Examples of Multivariate testing

Below are a few examples that would get you started with the multivariate testing of your website.

Landing Page Optimization

Goal: Increase conversion rate (e.g., sales, sign-ups)

Possible Variables: Headline, hero image, call-to-action button (text and color), tag lines, pricing display

Website Navigation

Goal: Enhance user experience and reduce bounce rates

Possible Variables: Menu structure, navigation labels, search bar placement, website layout and design elements

Email Marketing Campaign

Goal: Boost email open rates and click-through rates

Possible Variables: Subject line, sender name, email body content, call-to-action button (text and placement), images or visuals

Mobile App Onboarding

Goal: Improve user retention and engagement within a mobile app

Possible Variables: Welcome screen design, onboarding flow, feature highlights, tutorial elements

E-commerce Product Page

Goal: Improve product page conversion rate

Possible Variables: Product image layout, product description length and style, pricing and discount display, add-to-cart button placement, customer reviews or testimonials


Goal: Maximize ad engagement and conversions

Possible variations: Ad creatives (images, videos, graphics), Ad copy variations (tone, length, messaging), Call-to-action, Targeting options 

Top 10 multivariate testing tools

1. Optimizely: A robust platform for advanced experimentation, ideal for larger enterprises.

2. Google Optimize & Optimize 360: Free (Optimize) and paid (Optimize 360) plans offering A/B and multivariate testing, with Google Analytics integration.

3. VWO: Affordable and user-friendly option for marketers and ecommerce businesses.

4. Adobe Target: Feature-rich enterprise solution with personalization and omnichannel capabilities.

5. Qubit: Strong on revenue optimization and personalization, popular with travel and retail industries.

6. Unbounce: Landing page builder with built-in A/B and multivariate testing features.

7. Instapage: Focuses on high-converting landing pages with A/B testing capabilities.

8. Pagewiz: Offers real-time CRM integration and lead management alongside testing functionalities.

9. Kameleoon: Known for its visual editor and ease of use, suitable for beginners.

10. Convert Experiences: Provides comprehensive experimentation toolkit with advanced statistical analysis.

Take Away from Multivariate Testing

Multivariate testing (MVT) goes beyond A/B testing to show how multiple elements interact to influence user behavior and conversion rates. You get to discover not just the best individual element, but also the most effective combinations. 

When you should consider multivariate testing?

  • When you want to identify the most effective combinations of elements to maximize your goals.
  • When you have the resources and expertise to conduct and interpret MVT results effectively.
  • When you have sufficient website traffic to support statistically significant findings from multivariate testing.
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