Design Metrics and Impact Measurement
Connecting design work to measurable outcomes: choosing success metrics, KPIs and guardrails for a specific change, writing measurable problem statements and testable hypotheses, and judging when quantitative data should and should not override design judgment. Covers instrumentation and tracking plans (event taxonomy, identity resolution across platforms, data quality and privacy constraints), reading funnels, cohorts, retention and adoption curves, and reconciling behavioral analytics with survey and qualitative signal. Experimentation is a large part of this topic: A/B, multivariate and quasi-experimental design, sample size and minimum detectable effect, stopping rules, multiple-comparison and confounding traps, and isolating a design's effect when a clean test is not possible. Also covers post-launch monitoring, rollback criteria and post-mortems, ROI and business cases for design work including design systems and design programs, and reporting impact to executives and stakeholders.
