Defines north-star and guardrail metrics with owners, formulas, data sources, review cadence, and breach responses so MVP success is observable rather than debated after launch.
Package status: AI-assisted reference package. Static package validation has passed; implementation scenarios remain not run and human domain review is required before stable adoption.
Decision
Measure MVP success with a small set of decision-grade metrics: one north-star outcome, supporting input metrics, and guardrails that halt rollout when harm signals appear. Each metric is owned, computed from named data sources, and reviewed on a fixed cadence so product and engineering share the same evidence.
Scope
- Metric definitions, formulas, units, segmentation rules, and data-source mapping.
- Ownership, review cadence, dashboard locations, and breach response playbooks.
- Relationship between metrics and in-scope MVP requirements.
- Privacy purpose and pseudonymization boundaries for metric events.
Outside this block
- Full enterprise analytics platform design.
- Experimentation statistics methodology beyond guardrail monitoring.
- Event envelope schema details (see analytics event taxonomy reference block).
Contract
- Every adopted metric has a stable metric ID, human-readable name, formula, owner team, and primary data source.
- North-star metrics map to at least one in-scope user journey from the MVP scope block or document the mapping gap explicitly.
- Guardrail metrics define explicit breach thresholds and a mandatory response within a documented time window (profile choice).
- Metric changes version the formula and retain historical comparability notes; silent redefinition is prohibited.
- Missing or stale data beyond the profile staleness window blocks release decisions unless a time-bounded waiver from the metric owner is recorded with required audit fields.
- Segmentation dimensions used in decisions are enumerated; ad hoc segments cannot override published results without review.
Implementation guidance
- Implement metrics as code or configuration reviewed like application logic.
- Align event names with the organization event taxonomy where one exists.
- Publish a metric catalog consumable by dashboards and AI planning tools.
- Document known blind spots and proxy metrics when direct measurement is impossible at MVP stage.
Failure handling and safeguards
- On data pipeline failure, surface degraded-mode banners on decision dashboards and freeze auto-rollout triggers.
- If a guardrail breaches during rollout, execute the documented rollback or pause playbook before continuing.
- Disputed metric results require reproduction steps and owner sign-off; do not average conflicting definitions.
Verification and operations
- Monitor metric freshness, null rates, and week-over-week variance anomalies.
- Audit quarterly that formulas match implemented queries.
- Record decision outcomes linked to metric snapshots for post-launch learning.
The executable-looking examples in this package are fixtures and acceptance contracts. Validate package structure before adoption; then implement and execute the scenarios in the target repository.
Adoption assumptions
- Thresholds, cadences, and segment names in
example.yamlreflect profile choices for a team-workspace MVP, not universal defaults. - Teams must map identifiers to their privacy classification and retention policies.
References
- No external normative source is required for the measurement framework itself; align event payloads with your event taxonomy standard when available.