How should you design A/B/n tests in conversion rate optimization?

Short answer: A sound A/B/n test is built on one clear hypothesis, a pre-declared primary metric and a sufficient sample size; peeking before the test ends and deciding early is the most common mistake.

Setup steps

• Hypothesis: write which change will move which metric and why
• Primary metric: pick one decision metric and track secondaries separately
• Sample and duration: compute the minimum sample and a duration covering a full business cycle before you start
• Traffic split: distribute variants simultaneously and at random
• Integrity: one variable per test; apply a correction for many-armed tests

No winner is declared before statistical significance is reached. TYS Digital Performance sets up tests with SEO-safe release, measures with GA4 events and reports against the baseline. No invented uplift percentage is given; only the measured difference and confidence interval are shared.