Multivariate test
Testing combinations of several page changes at once to learn which elements, and which pairings, improve the result.
See it
What it is
A multivariate test changes several page elements at the same time and sends traffic across every planned combination. Three headlines, two images, and two CTAs make 12 versions. Unlike a simple A/B test, it can show both which element mattered and whether two choices work better together than either does alone.
Reach for it when you have heavy, steady traffic and a page made of distinct parts you can vary independently. A full-factorial design tests every combination; a fractional-factorial design tests a chosen subset when the full grid would be too large. VWO and Adobe Target both ship a multivariate mode alongside plain A/B.
Gotcha: combinations multiply much faster than traffic does. Splitting 10,000 visits across 12 versions can leave every result too noisy to trust, and changing the test midway ruins the clean comparison. Most small sites will learn faster from a sequence of focused A/B tests.
Ask AI for it
Design a full-factorial multivariate test for [page] using [traffic volume and baseline conversion rate]. Test exactly three factors: [headline variants], [hero variants], and [CTA variants]. Enumerate every combination, define one primary conversion event, randomize eligible visitors once and keep them in the same cell, then calculate traffic per cell and estimated runtime at alpha 0.05 and 80% power. Return the test matrix, implementation notes, an interaction-analysis plan, and a stop rule. If the full-factorial design is underpowered, replace it with a named fractional-factorial design and state exactly which interactions it cannot estimate.