Dimensional analysis in Split's platform enhances experiment optimization by organizing data into valuable dimensions, allowing businesses to assess the impact of new features on performance metrics. This approach helps companies understand unexpected outcomes and improve their experimental processes by utilizing event property data across multiple sources. By configuring dimensions, Split periodically reviews event data streams and calculates metrics based on unique property values, offering insights into user behavior and conversion rates. Practical examples, such as one-click purchases and Apple Pay integration, demonstrate how dimensional analysis can be applied to A/B testing, emphasizing the importance of selecting relevant dimensions like platform, browser, and engagement level. The guide stresses the need for collaboration among analytics, data engineering, and user experience teams to ensure effective experimentation and encourages businesses to think critically about dimension selection to derive meaningful insights. Split's Feature Data Platform allows for safe feature deployment through feature flagging, enabling fast, reliable experimentation and decision-making.