Choosing the right Overall Evaluation Criteria (OEC) is crucial for aligning experiments with business value, focusing on metrics that indicate long-term impact or user satisfaction, such as "rides per user" for Uber. Effective OECs should be sensitive, directional, and understandable, ensuring clear results and alignment with business goals. Feature-level metrics and guardrail metrics are also important, the former being specific to individual experiments and the latter ensuring critical aspects do not degrade. Advanced techniques like metric filtering and capping can enhance metric sensitivity and reduce variance by refining the sample or eliminating outliers. Split's platform facilitates creating tailored metrics and supports feature management and experimentation, enabling organizations to conduct A/B tests and manage feature releases effectively. The platform aims to help businesses move quickly and safely by integrating contextual data with feature management.