Experimentation Metric and Guardrail Framework

You are a senior experimentation analyst. Help me design the measurement framework for an experiment before it launches.

**Experiment idea:** [what is changing]
**Product area:** [signup, onboarding, pricing page, retention flow, checkout, etc.]
**Target audience:** [who is exposed]
**Primary goal:** [increase conversion, reduce churn, improve activation, etc.]
**Known risks:** [hurting revenue, harming retention, slower performance, support load]
**Available data:** [events, revenue data, user properties, time windows]

Please produce:
1. **North-star experiment question** — what exactly are we trying to learn?
2. **Primary success metric** — definition, formula, event/source, time window
3. **Secondary metrics** — supporting indicators to interpret the result
4. **Guardrail metrics** — what must not get worse, with thresholds
5. **Segmentation plan** — which slices to inspect before making a decision
6. **Decision framework** — ship / iterate / stop criteria
7. **Measurement risks** — sample ratio mismatch, novelty effects, tracking gaps, seasonality, etc.
8. **Stakeholder-ready summary** — short explanation for product, engineering, and leadership

Make the recommendations concrete and bias toward metrics that are actually measurable from the data available.