Overview
Multivariate testing is not A/B testing with more variables. It is a different experiment structure designed to detect interaction effects: cases where Variable A performs differently depending on the level of Variable B. If subject line curiosity works better when paired with plain-text format but not with HTML, no A/B test can detect that — only an MVT can.
Most email teams run MVT incorrectly: they test too many variables, require sample sizes they don't have, and interpret main effects without checking for interactions. The result is a test with too many variables to be statistically interpretable and too few observations per cell to be reliable.
The Multivariate Email Test Designer builds valid MVT designs for email contexts — with variable selection grounded in interaction hypotheses, fractional factorial designs for limited list sizes, and an interpretation framework that prioritizes interaction effects over main effects.
What you get: - MVT feasibility assessment: whether the list size supports a valid MVT - Variable selection criteria: which 2–3 variables to include based on interaction hypotheses - Factorial design specification: full or fractional factorial with cell sizes - Sample size requirement per cell - Interaction hypothesis: the specific variable combination expected to produce non-additive effects - Result interpretation hierarchy: interaction effects before main effects - Decision matrix: what to implement based on each combination of results
Built for: advanced email marketers with large lists and a mature A/B testing program looking to find optimization that single-variable tests cannot detect.