Multivariate testing
Definition
Multivariate testing (MVT) is an experimental method in which multiple variables are tested simultaneously across many combinations, to identify which combination of elements produces the best outcome on a defined metric.
In a standard A/B test, you change one thing — headline, button color, layout — and test it against the control. Multivariate testing changes several things at once and tests all combinations. If you're testing 3 headlines, 2 button colors, and 2 images, multivariate testing generates 12 combinations and runs them all.
The upside of multivariate testing: it surfaces interaction effects. Maybe headline A works best with image 1, but headline B works best with image 2. An A/B test on headlines alone would miss this. MVT finds the best combination, not just the best individual element.
The downside: it requires a lot more traffic. To reach statistical significance across 12 combinations rather than 2, you need roughly 6x the sample size. Most products don't have that traffic, which is why A/B testing is more commonly used.
Multivariate testing is best suited for high-traffic surfaces — homepage hero sections, email templates with large lists, checkout flows with millions of monthly visitors. For lower-traffic products, sequential A/B tests (one variable at a time) are more practical.
Tools that support multivariate testing include Optimizely, VWO, and Google Optimize (discontinued), along with custom implementations in analytics platforms.
Examples
- Testing three hero headlines, two CTA button labels, and two image styles on a high-traffic landing page
- Running 8 combinations of an email campaign (subject, preview text, CTA) on a large subscriber list
- Testing layout and copy variations simultaneously on a pricing page
- Using MVT to find the best onboarding screen combination across headline, illustration, and CTA
Related terms
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