Question 1

How long does a typical A/B test need to run?

Accepted Answer

Most tests need 2–4 weeks to reach statistical significance, depending on your traffic volume and the size of the effect. We calculate the required duration before launch — if your traffic can't support a meaningful test, we'll tell you and suggest higher-impact alternatives like radical redesigns or qualitative research.

Question 2

How much traffic do I need for A/B testing?

Accepted Answer

As a rule of thumb, you need at least 1,000 conversions per month on the page being tested to detect a 10% lift in a reasonable time. With less traffic we shift strategy: fewer, bolder tests with bigger expected effects, or testing higher-traffic pages first.

Question 3

Which testing tools do you work with?

Accepted Answer

We work with VWO, Optimizely, Convert, AB Tasty, and Growthbook, and we build server-side experiments with feature-flag platforms like LaunchDarkly. If you don't have a tool yet, we'll recommend one that fits your stack, traffic, and budget — including capable low-cost options.

Question 4

Will A/B tests slow down my website?

Accepted Answer

Done properly, no. We use anti-flicker techniques sparingly, load variant code asynchronously, and prefer server-side testing for performance-critical pages. We also monitor Core Web Vitals as guardrail metrics during every experiment.

Question 5

What's the difference between A/B and multivariate testing?

Accepted Answer

An A/B test compares complete page versions, while a multivariate test measures how several smaller elements interact — for example three headlines combined with two images. Multivariate tests need significantly more traffic, so we typically reserve them for high-volume pages.

Question 6

What happens if a test loses?

Accepted Answer

A losing test is still a win: it stopped you from shipping a change that would have hurt conversions, and it teaches you something real about your audience. Roughly half of all tests lose — that's exactly why testing matters. Every result is documented in your learnings library.

Question 7

Can you test changes deeper than the UI, like pricing or algorithms?

Accepted Answer

Yes. With server-side experimentation we can test pricing structures, search rankings, recommendation logic, onboarding flows, and email sequences. These deeper tests often produce far larger business impact than surface-level UI changes.

Question 8

How do you report results?

Accepted Answer

You get a plain-language summary with the conversion lift, confidence level, segment breakdowns, and revenue projection — plus the raw statistics for your analysts. Every test ends with an explicit recommendation, never just a dashboard screenshot.

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