Experimentation Metrics Pipeline
Implemented an experimentation-ready metrics pipeline for faster product decisions.
Overview
Experimentation Metrics Pipeline standardizes metrics and analysis so teams can run trustworthy A/B tests without spending weeks debating definitions. The focus was metric governance, reusable analysis helpers, and fast visibility into impact.
Goal: accelerate experimentation velocity while improving trust. Constraint: ensure every metric has a clear owner and definition.
What shipped
- Canonical metric catalog: definitions, owners, and guardrails
- Data lineage: where each metric comes from and how it's computed
- Reusable queries: cohorts, funnels, retention, and exposures
- Dashboards: guardrails (errors/latency) + business impact
Analysis workflow
Built a repeatable workflow that makes experiments easier to review: pre-registration template, success metric checklist, and consistent reporting format.
- Design: hypothesis + success criteria
- Execute: exposure checks + sample ratio monitoring
- Readout: impact summary with links and next actions
Rich content examples
Readouts include headings, ordered lists, and rich emphasis: bold conclusions, italic caveats, underlined decisions, and links to supporting artifacts like metric catalog and experiment template.