A/B testing: definition, method and best practices

A/B testing, also known as split testing, randomly divides incoming traffic into two groups: group A receives the original version (control) and group B receives the variant. The goal is to measure the variant's impact on a conversion indicator defined in advance, such as conversion rate, click-through rate, or revenue per visitor.

A crucial point: the hypothesis must be defined before the test, not after. Testing without a hypothesis leads to confirmation bias. You should also avoid stopping the test prematurely as soon as you observe a favorable result, because natural traffic fluctuations can distort conclusions.

The minimum recommended duration is one to two weeks to smooth out cyclical variations (weekday vs. weekend). Statistical significance at 95% is the standard threshold before drawing conclusions.

The variables to test as a priority are:

• The headline and hook copy
• The CTA (text, color, position)
• The main visual
• The layout and visual hierarchy
• The form (number of fields, labels)

A/B testing turns marketing and product decisions into evidence-based decisions rather than gut-feel ones. It enables gradual, measurable improvements in conversion rates, revenue per visitor, and user engagement.

Without systematic testing, teams often invest in changes that bring no real benefit, or even degrade performance. A/B testing is particularly valuable for high-traffic pages — landing pages, homepage, checkout — where a few percentage-point improvements in conversion translate into significant economic impact.

It is also the most reliable tool for validating insights from heatmaps and behavioral analyses.

1. 1Identify priorities via your analytics: high-bounce-rate pages, high-drop-off funnels, steps with a sharp fall in conversion rate.
2. 2Formulate a precise hypothesis: "Changing the CTA from X to Y will increase the click-through rate by Z% because…"
3. 3Calculate the required sample size before launching to ensure statistical power.
4. 4Modify only one variable at a time to isolate the causal effect.
5. 5Wait for statistical significance at 95% before concluding — never stop the test early.
6. 6Document every result, including failures: negative tests are just as instructive as positive ones.