OKR Design & Metrics Framework

OKR设计与度量框架

Structure goals, decompose metrics into KPI trees, identify leading indicators, and design rigorous experiments.

结构化目标，将指标拆解为KPI树，识别领先指标，并设计严谨的实验。

OKR Structure

OKR结构

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]

markdown

undefined

Objective（目标）是定性且具有激励性的。Key Result（关键结果）是定量且聚焦结果的——绝不能是产出列表。

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]

markdown

undefined

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]

undefined

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]

undefined

OKR Quality Checks

OKR质量检查

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.

检查项	Objective（目标）	Key Result（关键结果）
包含数值	否	是
具有激励性	是	无需
聚焦结果（而非“交付X个功能”）	是	是
70%可达成（具有挑战性，而非保守）	是	是
与更高层级目标对齐	是	是

查看references/okr-workshop-guide.md获取完整的引导议程（3-4小时，点投票，终稿模板）。查看rules/metrics-okr.md了解常见陷阱和对齐层级模式。

KPI Tree & North Star

KPI树与北极星指标

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)

将顶层指标拆解为具有明确因果关系的组件。

Revenue (Lagging — root)
├── New Revenue = Leads × Conv Rate          (Leading)
├── Expansion   = Users × Upsell Rate        (Leading)
└── Retained    = Existing × (1 - Churn)     (Lagging)

North Star + Input Metrics Template

北极星指标+输入指标模板

markdown

undefined

markdown

undefined

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

undefined

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

undefined

North Star Selection by Business Type

按业务类型选择北极星指标

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.

业务类型	北极星指标示例	原因
SaaS	Weekly Active Users	反映持续的价值交付
交易平台	Gross Merchandise Value	覆盖买卖双方业务
媒体	Time spent	参与度体现内容价值
电商	Purchase frequency	复购代表用户满意度

查看rules/metrics-kpi-trees.md获取完整的营收和产品健康度KPI树示例。

Leading vs Lagging Indicators

领先指标vs滞后指标

Every lagging metric you want to improve needs 2-3 leading predictors.

markdown

undefined

每个你希望提升的滞后指标都需要搭配2-3个领先预测指标。

markdown

undefined

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](rules/metrics-leading-lagging.md) for a balanced dashboard template.

---

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)


| 指标类型 | 回顾频率 | 行动周期 |
|-----------|----------------|-----------------|
| 领先指标 | 每日/每周 | 即时调整方向 |
| 滞后指标 | 每月/每季度 | 战略层面调整 |

查看[rules/metrics-leading-lagging.md](rules/metrics-leading-lagging.md)获取平衡仪表盘模板。

---

Metric Instrumentation

指标埋点

Every metric needs a formal definition before instrumentation.

markdown

undefined

每个指标在埋点前都需要有正式的定义。

markdown

undefined

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](rules/metrics-instrumentation.md) for the full metric definition template, alerting thresholds, and dashboard design principles.

---

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 }


事件命名规则：采用蛇形命名法`object_action`——例如`user_signed_up`、`feature_activated`、`subscription_upgraded`。

查看[rules/metrics-instrumentation.md](rules/metrics-instrumentation.md)获取完整的指标定义模板、告警阈值和仪表盘设计原则。

---

Experiment Design

实验设计

Every experiment must define guardrail metrics before launch. Guardrails prevent shipping a "win" that causes hidden damage.

markdown

undefined

每个实验在启动前必须定义防护指标（Guardrail）。防护指标可避免上线看似“成功”但实则造成隐性损害的功能。

markdown

undefined

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]

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]

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

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.

undefined

Any guardrail degrades > [threshold]% relative to baseline.

undefined

Pre-Launch Checklist

启动前检查清单

Hypothesis documented with expected effect size
Primary, secondary, and guardrail metrics defined
Sample size calculated for minimum detectable effect
Dashboard or alerts configured for guardrail metrics
Staged rollout plan with kill criteria at each stage
Rollback procedure documented

See rules/metrics-experiment-design.md for guardrail thresholds, performance and business guardrail tables, and alert SLAs.

假设已记录，包含预期影响幅度
已定义主要、次要和防护指标
已计算达到最小可检测效果所需的样本量
已配置防护指标的仪表盘或告警
已制定分阶段上线计划，每个阶段都有终止标准
已记录回滚流程

查看rules/metrics-experiment-design.md获取防护指标阈值、性能和业务防护指标表，以及告警服务级别协议（SLA）。

Common Pitfalls

常见陷阱

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

陷阱	应对措施
KR为产出（如“交付5个功能”）	改写为结果导向（如“提升转化率20%”）
仅跟踪滞后指标	每个滞后指标搭配2-3个领先预测指标
设定目标前无基线数据	在设定OKR前，先埋点并度量2周数据
无防护指标就启动实验	在任何代码上线前定义防护指标
OKR数量过多（团队超过5个）	限制为3-5个Objective，每个Objective搭配3-5个Key Result
指标无负责人	每个指标都需要对应团队负责人

okr-design

Original

Translation

OKR Design & Metrics Framework

OKR设计与度量框架

OKR Structure

OKR结构

Q1 OKRs

Q1 OKRs

Objective: Become the go-to platform for enterprise teams

Objective: Become the go-to platform for enterprise teams

OKR Quality Checks

OKR质量检查

KPI Tree & North Star

KPI树与北极星指标

North Star + Input Metrics Template

北极星指标+输入指标模板

Metrics Framework

Metrics Framework

North Star Selection by Business Type

按业务类型选择北极星指标

Leading vs Lagging Indicators

领先指标vs滞后指标

Metric Pairs

Metric Pairs

Metric Instrumentation

指标埋点

Metric: Feature Adoption Rate

Metric: Feature Adoption Rate

Experiment Design

实验设计

Experiment: [Name]

Experiment: [Name]

Hypothesis

Hypothesis

Metrics

Metrics

Design

Design

Rollout Plan

Rollout Plan

Kill Criteria

Kill Criteria

Pre-Launch Checklist

启动前检查清单

Common Pitfalls

常见陷阱

Related Skills

相关技能