Test multiple page
elements at once.

Multivariate testing (MVT) is an experimentation method that tests several page elements at once, in every combination, to find which mix performs best. Where an A/B test compares whole versions, a multivariate test isolates how elements interact: testing 2 headlines against 3 hero images creates 6 combinations, and the report tells you which pairing wins and how much each element contributed. Convert Experiences runs multivariate tests as a full factorial design, automatically generating every combination of the sections and variations you define and reporting performance at the combination level. It’s included in the Pro and Enterprise plans as of 2026, as part of the feature set rather than a paid add-on. For the concept in depth, including when MVT beats sequential A/B testing, read the complete multivariate testing guide.

Multivariate testing is the right choice when a page already converts, when you want to understand how elements interact, and when you have sufficient traffic to split across combinations. A/B testing is the right choice when you’re exploring a major directional change, when traffic is limited (under roughly five thousand visits per month), or when you have a single clear hypothesis to validate. Multivariate testing answers which combination wins; A/B testing answers which version wins.

A full factorial design tests every possible combination of your elements, so a 2-headline by 3-image test runs all 6 pairings and measures each interaction directly. A fractional factorial design tests only a subset of combinations and statistically infers the rest, which needs less traffic but assumes some interactions don’t matter, an assumption that’s exactly what a multivariate test exists to check. Convert Experiences uses full factorial design: every combination gets measured, no interaction gets assumed, and the trade-off is that you should size traffic accordingly (see the next question).

There’s no single fixed minimum, because the real requirement depends on baseline conversion rate, expected lift, number of combinations, and the statistical model used. A documented rule of thumb in Convert’s analysis guidance is at least 1,000 users and at least 100 conversions per combination, over at least one to two weeks. A 2-section by 3-variation test produces six combinations, which translates to roughly 6,000 minimum weekly users as a starting point.

Every combination you test is another chance for random noise to look like a winner: comparing six combinations against a control at 95% confidence inflates the odds of at least one false positive well past 5%. Multiple-comparison corrections tighten the threshold to keep the overall error rate honest. Convert offers three options on the Frequentist report (Bonferroni, Šidák, or none), user-configurable rather than automatic, and Convert’s documentation recommends Šidák for mission-critical experiments because it controls the family-wise error rate while preserving more statistical power than Bonferroni.

For typical content, design, and copy changes, a multivariate test is built end-to-end in the Visual Editor without writing code; the Convert tracking snippet on your site is the only requirement. Custom JavaScript components and single-page applications may need configuration beyond the snippet. For backend logic, Convert supports server-side experimentation through 6 SDKs as a Full-Stack experiment type, where you model combinations explicitly in your variation logic; that path is possible by design, though it isn’t a native UI workflow equivalent to the Visual Editor experience.

The Experience Report shows performance at the combination level, and you can switch the statistical model on a running experiment via Stats and Settings; moving from Bayesian to Frequentist removes the Bayesian-only Risk and Expected Uplift columns, and multivariate tests using Auto-Allocation (multi-armed bandit) on the Frequentist model require Sequential testing. When a winner emerges, Smart Recommendations can surface which variation is winning and by how much (opt-in via Project Configuration), and nothing deploys without consent: you either keep the winning variation running after the experiment ends or create a Deploy from it through the report.

Convert’s support is staffed by senior technical advisors capable of understanding experiment design. The team works on targeting logic, implementation trade-offs, and experiment QA. But it does not assist with experimentation builds. We can also refer you to our Convert Certified Agency partners – if you are currently not working with an agency. As of 2026, our median 1st response time is a little less than 3 minutes.