An experiment that tests multiple variables simultaneously to understand which combination of design elements performs best. Unlike A/B testing, multivariate testing can reveal interaction effects between elements but requires larger traffic volumes to reach statistical significance.
Common contexts
- Testing combinations of hero image, headline copy, and CTA button color simultaneously on a high-traffic landing page
- Running a multivariate test to determine which pairing of onboarding step order and progress indicator style produces the highest completion rate
- Analyzing interaction effects between a pricing table layout and a social proof block to find the highest-converting combination
Use when
Use multivariate testing when you have high, consistent traffic and need to understand how design elements interact — if you can only test one variable at a time, you'll miss combinations where the individual elements are weak but together they outperform.
Avoid when
Don't run multivariate tests with insufficient traffic — an underpowered test produces statistically unreliable results that can confidently point you in the wrong direction, which is worse than no test at all.
The interaction effects in a multivariate test are usually more interesting than the winning combination — they reveal which design elements are interdependent and which are genuinely independent optimizations.
Real-world examples
- Netflix simultaneously tests combinations of thumbnail image, title typography, and synopsis length for each piece of content, using multivariate testing to personalise the browse experience at the individual user level.
- Amazon runs hundreds of concurrent multivariate tests on its homepage at any time, isolating the combined effects of headline copy, hero image, and CTA placement on downstream purchase conversion.
- Booking.com's experimentation team conducts over 1,000 tests per year, many multivariate, to continuously optimise conversion across headline copy, urgency signals, and photo ordering simultaneously.