What is Multivariate Testing?

While A/B testing compares two complete versions of a page, multivariate testing breaks the page into individual elements and tests combinations of those element variations. For example, you might test 3 different headlines, 2 different hero images, and 2 different CTA button colors simultaneously. This creates 3 x 2 x 2 = 12 unique combinations, each shown to a segment of your traffic.

The advantage of MVT over sequential A/B testing is efficiency: you can identify winning combinations and interaction effects in a single test rather than running a series of isolated experiments. If a particular headline works especially well with a specific image (an interaction effect), an A/B testing sequence might miss this synergy entirely.

The disadvantage is traffic requirements. Each combination needs enough visitors to reach statistical significance independently. With 12 combinations, you need roughly 12 times the traffic that a simple A/B test would require. For smaller stores, this can mean tests that run for months before producing reliable results, which makes MVT impractical for sites with fewer than 50,000 monthly visitors.

Full factorial MVT tests every possible combination, while fractional factorial designs test a strategically selected subset and use statistical modeling to infer the performance of untested combinations. Fractional designs require less traffic but introduce assumptions about interaction effects that may not hold. The choice between full and fractional depends on your traffic volume and how confident you need to be in the results.

Eevy AI takes multivariate optimization further by using a genetic algorithm instead of traditional MVT. Traditional MVT requires pre-defining all variants and running them simultaneously. Eevy evolves new variants over generations, combining successful traits and introducing mutations. This approach requires less traffic than full factorial MVT while still capturing interaction effects between variables.