AB Testing vs Multivariate Testing: Which is Right For You?
A/B testing and multivariate testing are two invaluable methods for optimizing websites. Choosing the right testing approach is crucial for maximizing results.
This article explains the key differences between A/B and multivariate testing to help marketers, website owners, and others determine which method best fits their needs.
We’ll examine the pros and cons of each technique, outline use cases where each excels, and provide actionable recommendations for implementing an effective testing strategy.
Whether you’re new to optimization or looking to improve an existing program, this guide will empower you to make informed testing decisions that pay dividends for your website’s performance.
Table of Contents
What is AB Testing?
A/B testing, also known as split testing, is a method of comparing two versions of a web page to see which performs better. The goal is to identify changes that positively influence user behavior and conversion rates.
To run an A/B test, you show a randomly selected group of site visitors version “A” of a page while the rest see version “B.” The two variants should be identical except for one isolated change, such as different headlines, call-to-action button colors, image choices, or layouts. You then analyze key metrics like click-through rates, time on page, and conversions to see which version outperforms.
See the example of the change below (the CTA buttons are in different colors).
For example, an e-commerce site could test a red “Buy Now” button against a green “Buy Now” button. If the green button leads to more purchases, the site owner would then replace the red button with the green to boost revenues. A/B testing reveals how subtle changes impact user responses, enabling data-driven refinements over guesswork.
What is Multivariate Testing?
Multivariate testing, also known as multivariant testing, tests multiple website variables simultaneously to determine the optimal combination for achieving a desired outcome. While A/B tests isolate a single change, multivariate tests vary multiple elements like images, copy, layouts, and calls-to-action to identify the variables with the greatest impact.
For instance, an e-commerce site could run a multivariate test with different product image sizes, headline phrasings, button colors, and banner placements. The test would reveal how each element alone and in combination affects key metrics like add-to-cart rates and revenue per visitor.
The key purpose of multivariate testing is to understand the interaction effects between changes that may complement or inhibit each other. Testing variables together rather than in a series of isolated A/B tests can lead to better results through compounding gains. The insights allow sites to continually refine multiple parts of the user experience for optimization.
Key Differences Between AB Tests and Multivariate Tests
|AB Testing||Multivariate Testing|
|Tests one change at a time, such as different headlines or button colors.||Tests multiple changes simultaneously, like headlines, images, and calls to action.|
|Isolates the impact of a single variable.||Identifies interaction effects between variables.|
|Faster initial tests and iterations.||Requires more traffic to test multiple factors.|
|A/B tests require smaller sample sizes because they isolate single variable changes.||Multivariate tests need much larger sample sizes to test multiple variations and variable combinations.|
Traffic Needs For AB Testing
- A/B tests can work with relatively small sample sizes. Tests can run successfully with just a few thousand visitors to each variant.
- Simple A/B tests usually achieve statistical significance with sample sizes between 1,500-5,000 total users per variation. High-traffic sites can reach these numbers in a matter of days.
- Lower traffic sites may need A/B tests to run for a couple of weeks or more before enough data is collected. Tests should run until statistical significance is achieved.
- Too small of a sample size leads to more variability in results. Results may be inconclusive or skewed by outliers. Larger samples minimize the impact of outliers.
- Testing too many variations at once with limited traffic dilutes the sample size for each variant. This can prevent meaningful data from being collected on any single option.
Be strategic with the number of concurrent A/B tests to maintain sufficient sample sizes. Balance testing cadence with site traffic volumes.
Traffic Needs For Multivariate Testing
- Multivariate testing has much higher traffic demands than A/B testing due to the larger number of variations and the complex analysis:
- Each combination of variable changes is its own unique version. More variables mean exponentially more versions.
- Multivariate analysis requires far larger samples to achieve statistical significance across many variations.
- For accurate results, each variant needs thousands and more of conversions for high-value outcomes like sales or registrations.
- Tests may need to run for months to generate sufficient traffic volumes for the analysis. Lower-traffic sites may not be able to run multivariate tests.
- If sample sizes are too low, the probability of false positives or negatives increases substantially. Results become unreliable.
- Testing too many variations can stretch site traffic too thin, undermining data collection. A balance must be struck.
Time Needed For Results
Here is an overview of the typical time frames needed for conclusive results with A/B and multivariate testing:
- Most A/B tests yield actionable data within a few weeks. Simple tests can produce reliable results in just a few days.
- Run tests for 1-2 weeks minimum. Higher-traffic sites like Booking.com, Netflix, etc may need only 2-3 days.
- Let tests run until statistical significance is achieved. Don’t stop at arbitrary periods.
- Faster results are possible for high-traffic pages with obvious strong-performing variants.
- Expect multivariate tests to run for 1-2 month in most cases. Shorter tests are possible but not ideal.
- Tests should run until the platform’s algorithms determine statistical significance across variants.
- Testing duration correlates with number of variations, traffic volumes, and impact of changes.
- More radical changes may produce discernible results faster than subtle changes.
- Give tests adequate time to account for site traffic variances like day of week or season
How To Conduct An AB Test
1. Identify a High-Value Page
- Analyze Google Analytics data to identify high-traffic pages that also have high conversion rates or revenue potential.
- Focus on pages like product landing pages, shopping carts, contact forms, and other pages with large visitor volumes and frequent conversions.
- The more visitors a page gets and the more it contributes to key goals, the more optimization opportunities it presents for impactful A/B testing.
- If a page gets little traffic or has minimal conversion rates, any gains from testing will be small and not worth the effort. Prioritize pages with existing traction.
2. Define a Clear Goal Metric
- Clarify the specific user action, KPI or conversion metric you want to improve through A/B testing.
- Be as precise as possible. For example, “Increase signup conversion rate on the homepage” or “Reduce landing page bounce rate by 20%.”
- If you have multiple goals, prioritize the 1-2 with the highest revenue impact or strategic importance.
- Having a focused, measurable goal is crucial for shaping your hypothesis and accurately assessing whether a test succeeded.
3. Develop a Strong Hypothesis
- Create a clear “if X then Y” hypothesis stating how changing a one-page element will impact your defined goal metric. For example, “Changing the checkout button color from orange to green will increase e-commerce conversion rate by 15%.”
- Base your hypothesis on qualitative UX research insights or quantitative data analysis on-site behavior. Don’t guess.
- A strong hypothesis tests a specific causal relationship between a site change and the target metric.
4. Determine Appropriate Sample Size
- Use a statistical significance calculator to determine the minimum number of users needed per variation.
- Factor in your current conversion rate, expected effect size lift, and desired statistical confidence level.
- Samples that are too small risk inconclusive results due to insufficient data.
- Be prepared to run the test longer if needed to reach statistical significance. Don’t quit early.
5. Design Strong Variant Pages
- Work closely with designers and developers to create an excellent B variant incorporating your hypothesized change.
- Ensure the A and B variants are identical except for the one change being tested like button color, image, etc.
- Avoid unintended differences between versions that corrupt results. Rigorously QA both pages.
6. Configure the Testing Tool Properly
- Input power calculations, traffic splits, and targeting settings within your chosen testing tool.
- This ensures you are testing on appropriate samples with proper segmentation to answer your hypothesis.
- 50/50 splits between A and B variants are common. You can also allocate more traffic to the control.
- Use statistical significance calculators to inform tool configuration.
7. Check Quality Assurance
- Thoroughly QA the live page and the variant before launching the A/B test, especially any new code.
- Fix any issues, bugs or unintended differences between versions before launch to prevent corrupting results.
- Have testers mimic user behavior on both versions to surface any problems.
- Don’t launch until both pages function and appear as intended for visitors.
8. Analyze Results Daily
- Once launched, closely monitor key metrics and run statistical significance testing daily.
- Watch for early trends where one variation is clearly outperforming the other (don’t be in a haste to stop tests prematurely).
- Segment data by important categories like new vs. returning visitors, traffic source, geography to uncover insights.
- Build daily analysis into your team’s processes for optimized testing cadence.
9. Run Until Statistical Significance Reached
- Allow A/B tests to run until the minimum sample size is reached for your desired statistical confidence.
- Don’t make the mistake of stopping short based on an arbitrary time period rather than data.
- Statistical significance calculators tell you the necessary test duration to trust results.
- If needed, extend tests or reconfigure to gain more data. Don’t implement unreliable results.
10. Make Evidence-Based Decisions
- Analyze final A/B test results thoroughly before deciding on a “winning” variation.
- Only implement the new version if the measured lift is statistically significant. Beware false positives.
- If data is inconclusive, consider running the test again to gain confidence.
- Let data override opinions or intuition for evidence-based optimization.
How To Conduct A Multivariate Test
1. Identify a High-Value Page
- Analyze Google Analytics data to identify high-traffic pages that also have high conversion rates or revenue potential.
- Focus on pages like product pages, shopping carts, contact forms, and other pages with large visitor volumes and frequent conversions.
- The more visitors a page gets and the more it contributes to key goals, the more optimization potential through multivariate testing.
- Ensure the page gets enough consistent daily traffic to sustain a long-term test with multiple variations. Multivariate testing requires significant traffic for results.
- Lower traffic pages will take too long to conclude testing. Prioritize pages with existing traction for greater impact.
2. Define Success Metrics
- Clarify the specific conversion goals, KPIs or metrics you want to improve through multivariate testing.
- Be as precise as possible. For example, “Increase signup conversion rate by 25%” or “Lower bounce rate by 15%”.
- Focus on 1-2 core metrics that reflect business value and tie to overarching goals. Avoid diluting test efforts across too many metrics.
- Having clearly defined, measurable goals is crucial for shaping test variations and accurately assessing results.
- Consider high-level goals first, then drill down to specific quantification for optimization.
3. Map Page Elements to Test
- Conduct an in-depth audit of page content, layout, navigation, forms, images, calls-to-action, etc.
- Identify 6-10 elements on the page that are likely to impact user behavior and your success metrics based on research, data, and UX principles.
- Consider testing elements like headlines, value propositions, testimonials, visuals, information hierarchy, calls-to-action, content quality, etc.
- Be strategic about narrowing down the highest potential elements. Too many elements strain traffic and analytics.
4. Configure Test Parameters
- Use statistical significance calculators to determine appropriate confidence level, power, sample sizes, and test duration.
- Multivariate testing requires larger sample sizes than A/B testing due to more variations.
- Build the required traffic volumes and time duration into your test plans, especially for B2B sites.
- Take time to calculate significance upfront for reliable results properly. Don’t rush this step.
5. Design Test Versions
- Work closely with designers and developers to wireframe and create different version combinations of the page elements identified for testing.
- Rigorously QA all test versions to identify and fix any bugs or issues before launching live.
- Rely on the capabilities of multivariate testing platforms to efficiently serve versions and manage allocation.
6. Integrate with the Testing Platform
- Work with developers to fully integrate your page variations and success metrics with Convert, Optimizely, Adobe Target, or other multivariate testing tools.
- Thoroughly test integration to confirm the platform will appropriately distribute traffic across variants throughout the test duration.
- Develop a plan for activating and terminating the test while minimizing disruptions.
7. Monitor and Optimize Mid-Test
- Check results regularly to remove low-performing variations early to minimize effects.
- Allow top-performing variants to run longer and gain more data to amplify positive impacts.
- Make small tweaks to boost version differentiation without compromising test integrity.
8. Analyze Results Thoroughly
- After concluding the test, rigorously evaluate the statistical data to conclusively identify the optimal combination of page elements.
- Look for multivariate positive impacts that exceed the sum of individual element gains. This reveals beneficial interaction effects.
- Partner with data scientists and analysts to interpret complex results accurately based on significance.
When To Use AB Tests
A/B testing is ideal for pursuing smaller, incremental optimization gains through isolated changes. For example, an e-commerce site may want to test different combinations of button colors or minor layout changes to marginally improve conversion rates over time. Quick, iterative A/B tests allow for continual refinement rather than dramatic redesigns.
Early Stage Optimization
Companies just starting optimization often benefit from A/B testing to achieve some quick wins. This helps demonstrate value and build processes. Once the basics are covered, they can advance to more advanced multivariate testing. A travel site in the early days may simply test headline and call-to-action wording changes to identify improvements.
When wanting to precisely limit test complexity, A/B testing shines for isolating specific causes and effects between one change and the outcome. Multivariate testing can make it harder to pinpoint what element impacted results.
When To Use Multivariate Tests
Multivariate testing shines when an extensive site redesign is planned involving multiple changes across page layout, content, images, calls-to-action etc. Testing these elements in combination allows for dramatic iteration informed directly by data. For example, an ecommerce retailer undergoing a site-wide refresh tested product page designs incorporating layout, copy tone, imagery, reviews, and navigation changes together. This provided clearer validation of new templates than isolated A/B tests.
Some page types like calculators, configurators, Wizard-style questionnaires, and content-heavy pages have many elements that impact conversion together. Testing these pieces simultaneously rather than one at a time is more efficient and reveals synergistic effects between elements. A mortgage lender optimized its online application by testing field placement, help text, error messaging, images, and progress indicators in unison.
High Traffic Sites
Major enterprises with extremely high site traffic like Walmart, Amazon, and large publishers are ideal candidates for multivariate testing that requires significant visitor volumes continuously over long periods. These sites have both the conversion frequency and targeting capabilities to sustain the simultaneous testing of multiple variations. Multivariate provides them with ongoing impactful optimization.
Business-to-business (B2B) websites often contain complex pages for capabilities overviews, product research, contacting sales, and downloads. Optimizing these assets that drive leads in the long B2B sales cycle benefits greatly from multivariate testing combinations of copy, visuals, layouts, and calls-to-action. For example, an IT services firm tested web page content this way to reduce inquiry form abandonment.
Frequently Asked Questions On AB Testing vs Multivariate Testing
1. What is the main difference between A/B and multivariate testing?
The key difference is that A/B tests one isolated change between a control and variant, while multivariate tests multiple elements and combinations simultaneously.
2. When is A/B testing better to use than multivariate?
A/B testing works better for quick incremental optimization, early stage programs, simpler sites, and limiting test variables. It requires less traffic when compared to multivariate testing.
3. When should you choose multivariate testing over A/B?
Multivariate testing is better for radical redesigns, complex pages, large sites, cross-channel experiences, and maximizing impact.
4. What are the pros of A/B testing?
A/B testing is simple, fast, easy to set up, requires less traffic, and quickly provides learning via iteration.
5. What are the pros of multivariate testing?
Multivariate testing reveals interaction effects between elements, accommodates greater complexity, and supports holistic optimization.
6. How long should you run A/B vs multivariate tests?
A/B tests can run for weeks. Multivariate tests often take months due to more data needed. Set duration based on goals and traffic.
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