A/B Testing

Experimental method that compares two or more versions (A, B, C, etc.) of a digital element - web page, application, algorithm, banner ad, email, etc. - to determine which version performs better according to one or more defined indicators (conversion rate, click-through, revenue, engagement, etc.). - to determine which version performs best according to one or more defined indicators (conversion rate, clicks, revenues, engagement, etc.).

The different variants are randomly presented to users, then analyzed using rigorous statistical methods (frequentist or Bayesian) to ensure that the results observed are significant and reliable.

Unlike correlative or retrospective analyses, A/B testing establishes a causal relationship: it isolates precisely the impact of a modification on user behavior, by controlling external variables.

The A/B(n) approach is at the heart of Conversion Rate Optimization (CRO) strategies, because it allows :

• continuously improve site or product performance,
• limit the risks associated with untested deployments,
• prioritize developments that deliver measurable value,
• validate decisions on the basis of empirical data and figures.

In practice :

• An A/B test compares two variants (A vs B),
• An A/B/n test can compare three or more versions (A vs B vs C...), while maintaining a random distribution and consistent statistical analysis.

This method is commonly used in e-commerce, SaaS, media or mobile app contexts, and integrated into tools such as AB Tasty, Optimizely, VWO, Google Optimize (ex) or Dynamic Yield.

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