CAPM Practice: Validate requirements through product delivery

Usage analytics provide objective validation data: if a feature built to increase user engagement shows no change in engagement metrics, requirements may have been misaligned with actual user motivations. Analytics transform gut-feel requirements validation into evidence-based learning.

**Why not B:** Server throughput and requests-per-second metrics are performance or non-functional requirements validation, not requirements validation in the BA sense. Behavioral analytics focus on whether users actually adopt and benefit from a feature, which is a fundamentally different question from infrastructure capacity.

**Why not C:** Analytics are a powerful complement to requirements validation techniques such as reviews, walkthroughs, prototyping, and user testing — not a wholesale replacement for them. Some requirements (regulatory compliance, edge-case behaviors) cannot be validated through usage data alone.

**Why not D:** While analytics originated in marketing, product teams use behavioral data extensively to validate whether built features achieve their intended outcomes. Confining analytics to marketing ignores modern product management practice where data-driven validation is core to the development cycle.