A/B testing, also known as split testing, is a method used by companies to test different versions of a web page or product feature against the original to determine which version enhances user engagement and boosts key business metrics. This process involves gathering baseline data, formulating hypotheses, and using feature flags to manage and control the distribution of experimental features to targeted user segments. The implementation of feature flags facilitates the segmentation of users and the seamless rollout of experiments, allowing teams to monitor the impact on metrics through their analytics platforms. Advanced testing methods like A/B/n and multivariate testing enable the examination of multiple variants or combinations to optimize conversion rates and user experience, though they may require more time to achieve statistically significant results. Successful A/B testing not only improves user experiences and increases revenue but also enhances decision-making by correlating experimental data with company KPIs, making it a powerful tool for driving business impact.