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A GoodUI subscription unlocks the A/B test evidence behind every variable in this design, plus all of its evidence-backed defaults, so each choice is backed by data from similar experiments.

Beyond the review, we teach you or your team the reasoning behind it, so the next test is one you can design and judge yourselves. Pick whichever lessons you're interested in:

• Power for a continuous metric Size the test on a metric like product pages viewed, using its variance, not just a conversion rate.
• Behavioral metrics that explain why Track product pages opened, viewed and compared, so you see what happened and know what to try next.
• Power gains from triggering Only count visitors who actually click a result, so the effect isn't diluted by everyone else.
• Desktop and mobile apart Switching tabs is harder on phones, so break the result down by device.
• A sales guardrail More open tabs can mean more browsing, not more buying, so ship only if completed sales hold.
• Strength in numbers Combine your result with 8 past experiments to reach a decision faster.
• Reading A/B test results How to interpret significance, confidence intervals and what they do and don't tell you.
• Avoiding common testing traps Peeking, underpowered tests, metric soup and other pitfalls that quietly bias your reads.
• Choosing the right metrics Picking a primary metric and guardrails that match the decision you're actually making.
• Sizing and powering a test Baseline, MDE, power and runtime — how to know whether a test can be decisive before you launch.
• From result to next experiment Turning an outcome into the next hypothesis: what to keep, change or retest.

Hand it off. We design, implement, and QA the test, run and monitor it for you, then review the results and deliver a write-up — you get the outcome without the build effort.

• Metric alignment and setup Define the primary metric, guardrails, target audience, tracking events, attribution window and launch criteria so the test measures the decision you actually need to make.
• Power calc Estimate baseline conversion, minimum detectable effect, sample size and runtime before launch, with a clear read on whether the test is likely to be decisive or exploratory.
• Prototype-quality code Build the variation to prototype quality in your testing tool or front-end stack, with responsive states, analytics hooks, QA notes and handoff-friendly implementation details.
• Analysis Read the result with significance, confidence intervals, guardrail checks and segment caveats, then translate the stats into a practical ship, stop or iterate recommendation.
• Post-test and iteration recommendations Turn the result into next moves: what to keep, what to change, what to retest and which follow-up experiments are most worth running next.
• Discussions Use async or live discussion time to clarify assumptions before launch, review tradeoffs during the run and unpack ambiguous outcomes after the test closes.