A/B testing
Not 'design likes/dislikes', but statistically significant effect. All decisions — by data, not taste.
06Frequently asked questions
Frequently asked questions
How is A/B testing different from simple comparison?
A/B test is a controlled experiment with randomization: part of users see variant A, part — B, all else equal. Only then can you isolate the effect of the change. Simple 'before/after' doesn't work — seasonality, ads, algorithm changes affect conversion.
Which tests give the maximum effect?
From experience: (1) offer and headline changes — usually the strongest effect; (2) form simplification — direct impact on lead conversion; (3) social proof work (cases, reviews, logos); (4) page speed and mobile version. Design and color rarely rise above noise.
How much traffic is needed for a test?
Rule of thumb: minimum 1,000 conversions per variant or 10,000 visits, depending on current conversion. If traffic is lower — we work via sequential periods (not ideal but acceptable) or qualitative methods (interviews, UX audit).
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