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):
- New signups — acquisition
- Onboarding completion rate — activation
- Features used per user/week — engagement
- Invite rate — virality
- 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:
- Product usage frequency (weekly)
- Support ticket severity (daily)
- NPS score trend (monthly)
Lagging: Revenue Growth Leading:
- Pipeline value (weekly)
- Demo-to-trial conversion (weekly)
- 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
- 10% — 1 week canary, monitor guardrails daily
- 50% — 2 weeks, confirm statistical significance
- 100% — full rollout with continued monitoring
Kill Criteria
Any guardrail degrades > [threshold]% relative to baseline.
Pre-Launch Checklist
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Hypothesis documented with expected effect size
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Primary, secondary, and guardrail metrics defined
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Sample size calculated for minimum detectable effect
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Dashboard or alerts configured for guardrail metrics
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Staged rollout plan with kill criteria at each stage
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Rollback procedure documented
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
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prioritization — RICE, WSJF, ICE, MoSCoW scoring; OKRs define which KPIs drive RICE impact
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product-frameworks — Full PM toolkit: value prop, competitive analysis, user research, business case
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product-analytics — Instrument and query the metrics defined in OKR trees
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write-prd — Embed success metrics and experiment hypotheses into product requirements
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market-sizing — TAM/SAM/SOM that anchors North Star Metric targets
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competitive-analysis — Competitor benchmarks that inform KR targets
Version: 1.0.0