Multivariate Test

Multivariate Testing (MVT) is a quantitative research method used in UX (User Experience) research to test multiple variables or elements on a webpage or interface simultaneously. The goal is to understand how different combinations of these variables affect user behavior, allowing researchers and designers to identify the most effective design elements for improving usability, conversion rates, or other key metrics.

What is Multivariate Testing?

Multivariate testing is similar to A/B testing but more complex. While A/B testing compares two versions of a webpage or design (A vs. B), multivariate testing evaluates multiple elements at once. For example, you might simultaneously test different variations of headlines, images, and buttons to determine which combination performs best.

In MVT, different combinations of design elements are tested in various combinations, and the results are measured to see which combination achieves the desired outcome, such as higher engagement, conversions, or lower bounce rates.

Example of Multivariate Testing:

• Elements Tested: Button color, headline text, and background image.
• Combinations: You create variations for each of these elements (e.g., 3 button colors, 2 headlines, 2 background images), resulting in multiple combinations (3 x 2 x 2 = 12 combinations).
• Goal: Determine which combination of button color, headline, and background image leads to the highest click-through rate (CTR).

Differences Between Multivariate Testing and A/B Testing

A/B Testing:

• Tests two versions (A and B) to see which performs better.
• Only changes one element at a time (e.g., button color or headline).
• Simpler and provides clear results on a single variable's impact.

Multivariate Testing:

• Tests multiple elements (e.g., button color, headline, and image) simultaneously.
• More complex because it evaluates how combinations of elements work together.
• Provides insights into how different design elements interact and which combination has the greatest effect on user behavior.

How Multivariate Testing Works

1. Identify Variables: Choose the elements on a page that you want to test.

2. Create Variations: For each element, create different variations. For example, you might have three different headlines, two button colors, and two background images.

3. Generate Combinations: The variations are combined in different ways to create multiple versions of the page. For example, if you have 2 variations of the headline, 2 variations of the image, and 3 variations of the button, you’ll test 12 combinations (2 x 2 x 3 = 12).

4. Run the Test: Use a testing platform (e.g., Google Optimize, Optimizely, VWO) to serve different combinations of the variations to users. The platform will distribute traffic equally across the different combinations.

5. Measure Performance: Track the performance of each combination based on key metrics like click-through rates, conversions, time spent on page, etc.

6. Analyze Results: Identify which combination of elements led to the highest performance. Analyze not only the individual impact of each variable but also how they work together.

7. Implement Changes: Once the most effective combination is identified, implement the winning design elements on the live site.

Steps to Conduct a Multivariate Test

1- Define Your Objective

Before starting a multivariate test, determine what you want to achieve. Common objectives include:

• Increasing conversion rates
• Reducing bounce rates
• Improving engagement (e.g., more clicks on a CTA)
• Enhancing user satisfaction

2- Select Variables to Test

Identify which elements of the page you believe are most important for influencing user behavior. These could include:

• Headline (text or placement)
• CTA button (color, size, placement)
• Images (type of image, placement)
• Forms (field arrangement or number of fields)

3- Develop Hypotheses

Create hypotheses for each variable. For example:

• Headline: “A more action-oriented headline will increase engagement.”
• CTA Button: “Changing the button color to green will increase click-through rates.”
• Image: “A product image will resonate better with users than a generic stock photo.”

4- Create Variations

For each element, design different variations. For example:

• Headline: Version A: “Get Started Now” vs. Version B: “Start Your Free Trial Today”
• Button Color: Blue vs. Green
• Background Image: Product Image vs. Lifestyle Image

5- Set Up the Test

Use a testing platform to create all possible combinations of your variations and distribute traffic evenly to each version. Ensure that your sample size is large enough to produce statistically significant results.

6- Run the Test

Let the test run until you gather enough data to ensure the results are statistically significant. Multivariate tests usually require a large amount of traffic to generate meaningful results because multiple combinations are being tested simultaneously.

7- Analyze Results

Analyze the performance of each combination. Identify which combination of variables yielded the best results and whether any specific interaction between variables was responsible for the success.

8- Implement the Winning Combination

Once you’ve identified the best-performing combination, implement those changes across your design to improve user experience and achieve your objectives.

Tools for Multivariate Testing

Several tools are available for running multivariate tests, including:

• Optimizely: A popular experimentation platform that supports multivariate testing and real-time results tracking.
• VWO (Visual Website Optimizer): Another testing platform that allows for both A/B and multivariate tests with robust reporting and analytics.
• Adobe Target: An enterprise-level tool that supports advanced testing, including multivariate tests.

Best Practices for Multivariate Testing

1. Test Only Critical Elements: Multivariate testing can become complex quickly. Focus on testing only the most critical elements that you believe will have the biggest impact on user behavior.

2. Ensure Enough Traffic: Multivariate tests require significant traffic to produce statistically significant results. If you have low traffic, consider running an A/B test instead.

3. Run Tests for Long Enough: Ensure that your test runs long enough to account for variations in user behavior over time (e.g., weekdays vs. weekends).

4. Start Simple: If you’re new to multivariate testing, start with just a few variables to keep the test manageable. As you become more comfortable, you can increase the complexity.

5. Use Analytics to Guide Decisions: Use insights from your analytics platform to identify elements that may need optimization and should be tested.

6. Statistical Significance Matters: Don’t make decisions until your test results reach statistical significance. This ensures that your results are reliable and not due to chance.

Challenges of Multivariate Testing

1. Requires Large Traffic: Because multiple combinations are tested simultaneously, multivariate testing requires a significant amount of traffic to produce statistically valid results.

2. Complexity: As the number of variables increases, the complexity of the test grows exponentially. Managing and analyzing multiple combinations can be time-consuming.

3. Longer Timeframe: Multivariate tests can take longer to complete, especially if traffic is low or if many combinations are being tested.

4. Analysis Paralysis: With so many combinations and data points, it can be difficult to interpret the results and decide which changes to implement. Focusing on key performance metrics can help simplify the analysis.

When to Use Multivariate Testing

• Optimizing Complex Interfaces: When you have multiple design elements that you want to test simultaneously (e.g., headline, CTA, image), multivariate testing is the best method.
• Established Products with High Traffic: Multivariate testing is ideal for established websites or apps with sufficient traffic to ensure meaningful results.
• Identifying Interactions Between Elements: If you suspect that changes to one element might influence the performance of another (e.g., button color affecting the effectiveness of a headline), multivariate testing is the appropriate tool.