Glossary

How To Run an A/B Test

A/B testing is a practical, data-driven way to optimize marketing strategies and boost conversions by comparing two versions of a webpage, email, or ad to see which performs better. This step-by-step guide walks you through defining clear goals and hypotheses, choosing the right metrics and sample size, selecting tools, running tests, and interpreting results so you can iterate confidently. Along the way you’ll find actionable tips and best practices to avoid common pitfalls, ensure statistical validity, and scale successful variants across campaigns.

What is A/B Testing?

A/B testing is a controlled experiment that compares two or more versions of a webpage, email, app feature, ad, or other product element to determine which performs best.

Users are randomly assigned to variants (commonly “A” = control and “B” = variant), and their behavior is measured against predefined metrics such as conversion rate, click-through rate, revenue per visitor, and engagement. Statistical analysis determines whether observed differences are likely real or due to chance.

How it works

Form a clear hypothesis (e.g., “Changing the call-to-action color will increase sign-ups”).

Create at least two variants: the current version (control) and one or more test versions.

Randomly assign visitors to each variant to avoid selection bias.

Run the experiment for a statistically valid sample size and duration.

Compare KPI outcomes using appropriate statistical tests and confidence intervals.

Deploy the winning variant or iterate with new hypotheses.

Where it’s used

Landing pages and site UX

Pricing pages and checkout flows

Onboarding and product features

Email subject lines and content

Paid ads and creative

Key benefits

Data-driven decision-making instead of guesswork

Incremental, measurable improvements in conversion and revenue

Reduced rollout risk by validating changes before full deployment

Insights into user preferences and behavior

Common pitfalls to avoid

Small sample sizes or short test durations

Multiple simultaneous tests causing interaction effects

Testing without a clear hypothesis or meaningful metric

Ignoring segmentation and long-term impact

Why it matters

A/B testing turns product and marketing decisions into repeatable experiments, enabling teams to optimize performance, prioritize changes based on measurable impact, and scale improvements across channels.

How A/B Testing Works

A/B testing is a controlled experiment that compares two or more variants of a webpage, app feature, email, or other product element (typically an “A” control and a “B” variant) by randomly assigning users to each variant and measuring predefined metrics to determine which version performs better statistically.

Form a clear hypothesis: State the problem, the expected change, and a measurable outcome (e.g., “Changing the CTA color will increase the click-through rate by X%).”

Choose a primary metric and supporting KPIs: Pick one decisive metric (conversion rate, revenue per visitor) and track secondary metrics (bounce rate, time on page) to catch trade-offs.

Build variants: Create a control (A) and one or more variants (B, C) that differ only in the element(s) you are testing.

Randomly assign users: Split traffic randomly and evenly so each visitor has an equal chance of seeing any variant; randomization removes selection bias.

Ensure adequate sample size and test duration: Calculate the required sample size using baseline conversion, minimum detectable effect, confidence level, and statistical power; run long enough to cover traffic cycles (weekday/weekend).

Collect data and avoid peeking: Accumulate results without interim stopping for significance checks to prevent false positives; use preplanned stopping rules or sequential testing methods if needed.

Analyze with statistical rigor: Compare variants using appropriate tests (e.g., z-test, t-test, Bayesian methods). Report confidence intervals, p-values (or posterior probabilities), and practical significance, not just statistical significance.

Check segments and metrics holistically: Validate effects across key segments (device, geography, new versus returning) and review secondary KPIs to ensure no negative impacts.

Declare a winner and roll out: If a variant shows a reliable, positive effect, deploy it broadly; if results are inconclusive, iterate on the hypothesis and test again.

Iterate and scale: Use insights to inform future tests, build a testing roadmap, and scale successful changes across pages, channels, or campaigns.