Feature Rollout

Progressive rollout / gradual release: A method of activating a new functionality for a limited subset of users, prior to global deployment. In line with CRO andA/B testing, this approach enables us to measure the real impact of a change (on conversion rate, revenue, retention, etc.) while reducing technical and business risks. Progressive deployment is often controlled via feature flags and orchestrated in stages (e.g.: 1%, 5%, 20%, 50%, 100%).

However, it is essential to pay attention to the size of the populations exposed: samples that are too small can result in a high standard error (SREM), making statistical analyses unreliable. It is therefore advisable to combine this type of deployment with robust metrics (RPV, success rate per session, etc.) and a rigorous calculation of statistical confidence before drawing any conclusions.

Platforms such as ABtasty Feature Experimentation, Split.io, Optimizely or Firebase Remote Config can be used to manage these deployments, with integrated analytics.

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