A/B testing

A/B testing is a randomized controlled experiment: it compares two variants (A: Control, B: Variation) to determine which one produces a statistically significant lift in a key metric.

This methodology (also called split testing) is the definitive way to compare two versions of a digital asset: a webpage, an email subject line, or a mobile app feature. The process randomly splits user traffic (e.g., 50/50) between the Control and the Variation, measuring the impact on a specific business goal. By testing elements like a new call-to-action button color or a revised headline, teams move optimization from 'we think' to 'we know.' This data-backed approach ensures that only changes proven to increase conversion rate, click-through rate, or revenue per visitor are implemented, maximizing ROI.

https://www.optimizely.com/optimization-glossary/ab-testing/