OKR Design & Metrics Framework
Structure goals, decompose metrics into KPI trees, identify leading indicators, and design rigorous experiments.
OKR Structure
Objectives are qualitative and inspiring. Key Results are quantitative and outcome-focused — never a list of outputs.
Objective: Qualitative, inspiring goal (70% achievable stretch)
+-- Key Result 1: [Verb] [metric] from [baseline] to [target]
+-- Key Result 2: [Verb] [metric] from [baseline] to [target]
+-- Key Result 3: [Verb] [metric] from [baseline] to [target]
## Q1 OKRs
### Objective: Become the go-to platform for enterprise teams
Key Results:
- KR1: Increase enterprise NPS from 32 to 50
- KR2: Reduce time-to-value from 14 days to 3 days
- KR3: Achieve 95% feature adoption in first 30 days of onboarding
- KR4: Win 5 competitive displacements from [Competitor]
OKR Quality Checks
| Check | Objective | Key Result |
|---|
| Has a number | NO | YES |
| Inspiring / energizing | YES | not required |
| Outcome-focused (not "ship X features") | YES | YES |
| 70% achievable (stretch, not sandbagged) | YES | YES |
| Aligned to higher-level goal | YES | YES |
See references/okr-workshop-guide.md for a full facilitation agenda (3-4 hours, dot voting, finalization template).
See rules/metrics-okr.md for pitfalls and alignment cascade patterns.
KPI Tree & North Star
Decompose the top-level metric into components with clear cause-effect relationships.
Revenue (Lagging — root)
├── New Revenue = Leads × Conv Rate (Leading)
├── Expansion = Users × Upsell Rate (Leading)
└── Retained = Existing × (1 - Churn) (Lagging)
North Star + Input Metrics Template
## Metrics Framework
North Star: [One metric that captures core value — e.g., Weekly Active Teams]
Input Metrics (leading, actionable by teams):
1. New signups — acquisition
2. Onboarding completion rate — activation
3. Features used per user/week — engagement
4. Invite rate — virality
5. Upgrade rate — monetization
Lagging Validation (confirm inputs translate to value):
- Revenue growth
- Net retention rate
- Customer lifetime value
North Star Selection by Business Type
| Business | North Star Example | Why |
|---|
| SaaS | Weekly Active Users | Indicates ongoing value delivery |
| Marketplace | Gross Merchandise Value | Captures both buyer and seller sides |
| Media | Time spent | Engagement signals content value |
| E-commerce | Purchase frequency | Repeat = satisfaction |
See rules/metrics-kpi-trees.md for the full revenue and product health KPI tree examples.
Leading vs Lagging Indicators
Every lagging metric you want to improve needs 2-3 leading predictors.
## Metric Pairs
Lagging: Customer Churn Rate
Leading:
1. Product usage frequency (weekly)
2. Support ticket severity (daily)
3. NPS score trend (monthly)
Lagging: Revenue Growth
Leading:
1. Pipeline value (weekly)
2. Demo-to-trial conversion (weekly)
3. Feature adoption rate (weekly)
| Indicator | Review Cadence | Action Timeline |
|---|
| Leading | Daily / Weekly | Immediate course correction |
| Lagging | Monthly / Quarterly | Strategic adjustments |
See rules/metrics-leading-lagging.md for a balanced dashboard template.
Metric Instrumentation
Every metric needs a formal definition before instrumentation.
## Metric: Feature Adoption Rate
Definition: % of active users who used [feature] at least once in their first 30 days.
Formula: (Users who triggered feature_activated in first 30 days) / (Users who signed up)
Data Source: Analytics — feature_activated event
Segments: By plan tier, by signup cohort
Calculation: Daily
Review: Weekly
Events:
user_signed_up { user_id, plan_tier, signup_source }
feature_activated { user_id, feature_name, activation_method }
Event naming: object_action in snake_case — user_signed_up, feature_activated, subscription_upgraded.
See rules/metrics-instrumentation.md for the full metric definition template, alerting thresholds, and dashboard design principles.
Experiment Design
Every experiment must define guardrail metrics before launch. Guardrails prevent shipping a "win" that causes hidden damage.
## Experiment: [Name]
### Hypothesis
If we [change], then [primary metric] will [direction] by [amount]
because [reasoning based on evidence].
### Metrics
- Primary: [The metric you are trying to move]
- Secondary: [Supporting context metrics]
- Guardrails: [Metrics that MUST NOT degrade — define thresholds]
### Design
- Type: A/B test | multivariate | feature flag rollout
- Sample size: [N per variant — calculated for statistical power]
- Duration: [Minimum weeks to reach significance]
### Rollout Plan
1. 10% — 1 week canary, monitor guardrails daily
2. 50% — 2 weeks, confirm statistical significance
3. 100% — full rollout with continued monitoring
### Kill Criteria
Any guardrail degrades > [threshold]% relative to baseline.
Pre-Launch Checklist
See rules/metrics-experiment-design.md for guardrail thresholds, performance and business guardrail tables, and alert SLAs.
Common Pitfalls
| Pitfall | Mitigation |
|---|
| KRs are outputs ("ship 5 features") | Rewrite as outcomes ("increase conversion by 20%") |
| Tracking only lagging indicators | Pair every lagging metric with 2-3 leading predictors |
| No baseline before setting targets | Instrument and measure for 2 weeks before setting OKRs |
| Launching experiments without guardrails | Define guardrails before any code is shipped |
| Too many OKRs (>5 per team) | Limit to 3-5 objectives, 3-5 KRs each |
| Metrics without owners | Every metric needs a team owner |
Related Skills
prioritization — RICE, WSJF, ICE, MoSCoW scoring; OKRs define which KPIs drive RICE impactproduct-frameworks — Full PM toolkit: value prop, competitive analysis, user research, business caseproduct-analytics — Instrument and query the metrics defined in OKR treeswrite-prd — Embed success metrics and experiment hypotheses into product requirementsmarket-sizing — TAM/SAM/SOM that anchors North Star Metric targetscompetitive-analysis — Competitor benchmarks that inform KR targets
Version: 1.0.0
