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]

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

## 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

## 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)

## 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 }

## 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.

## Experiment: [Name]

### Hypothesis
If we [change], then [metric] will [direction] by [amount]
because [reasoning based on evidence].

### Metrics
- **Primary:** [The metric you are trying to move]
- **Secondary:** [Supporting metrics that add context]
- **Guardrails:** [Metrics that MUST NOT degrade -- see below]

### Design
- **Type:** A/B test | multivariate | feature flag rollout
- **Sample size:** [N per variant, calculated for statistical power]
- **Duration:** [Minimum weeks to reach significance]
- **Segments:** [User cohorts to analyze separately]

### Success Criteria
- Primary metric improves by >= [X]% with p < 0.05
- No guardrail metric degrades by > [threshold]
- Secondary metrics trend positive or neutral

### Rollout Plan
1. **10%** -- 1 week canary, monitor guardrails daily
2. **50%** -- 2 weeks, confirm statistical significance
3. **100%** -- full rollout with continued monitoring

## Guardrail: [Metric Name]

- **Baseline:** [Current value over last 30 days]
- **Warning:** [X% degradation from baseline]
- **Critical:** [Y% degradation -- auto-pause experiment]
- **Owner:** [Team or person who responds]
- **Response SLA:** Warning = 24h, Critical = 1h

Output (North Star): Weekly Active Teams
    |
    +-- Input: New team signups (acquisition)
    +-- Input: Teams completing onboarding (activation)
    +-- Input: Features used per team/week (engagement)
    +-- Input: Teams inviting members (virality)
    +-- Input: Teams upgrading to paid (monetization)

Experiment: New checkout flow
Hypothesis: Will increase conversions
Plan: Ship to 100% of users on Monday
Success: Conversions go up

Experiment: New checkout flow
Hypothesis: Reducing steps from 4 to 2 will increase
  checkout completion by 15% because 60% of users
  drop off between steps 2 and 3.
Primary: Checkout completion rate
Guardrails: Revenue per transaction, refund rate, page load p95
Rollout: 10% for 1 week, 50% for 2 weeks, 100%
Kill criteria: Any guardrail degrades > 5% relative

## Metric: [Name]

### Definition
[Precise definition of what this metric measures]

### Formula

### Data Source
- System: [Where data comes from]
- Table/Event: [Specific location]
- Owner: [Team responsible]

### Segments
- By customer tier (Free, Pro, Enterprise)
- By geography (NA, EMEA, APAC)
- By cohort (signup month)

### Frequency
- Calculation: Daily
- Review: Weekly

### Targets
| Period | Target | Stretch |
|--------|--------|---------|
| Q1 | 10,000 | 12,000 |
| Q2 | 15,000 | 18,000 |

### Related Metrics
- Leading: [Metric that predicts this]
- Lagging: [Metric this predicts]

[object]_[action]

Examples:
- user_signed_up
- feature_activated
- subscription_upgraded
- search_performed
- export_completed

{
  "event": "feature_activated",
  "timestamp": "2026-02-13T10:30:00Z",
  "user_id": "usr_123",
  "properties": {
    "feature_name": "advanced_search",
    "plan_tier": "pro",
    "activation_method": "onboarding_wizard"
  }
}

## Alert: [Metric Name]

| Threshold | Severity | Action |
|-----------|----------|--------|
| < Warning | Warning | Investigate within 24 hours |
| < Critical | Critical | Immediate escalation |
| > Spike | Info | Review for anomaly |

### Escalation Path
1. On-call engineer investigates
2. Team lead notified if not resolved in 2 hours
3. VP notified for P0 metrics breach

## Experiment: [Name]

### Hypothesis
We believe [change] will cause [metric] to [improve by X%]

### Success Metric
- Primary: [Metric to move]
- Guardrail: [Metric that must not degrade]

### Sample Size
- Minimum: [N] per variant
- Duration: [X] weeks
- Confidence: 95%

### Rollout Plan
1. 5% canary for 1 week
2. 25% for 2 weeks
3. 50% for 1 week
4. 100% rollout

{
  "event": "UserSignup",
  "event": "feature-activated",
  "event": "Subscription_Upgraded"
}

{
  "event": "user_signed_up",
  "event": "feature_activated",
  "event": "subscription_upgraded"
}

                         Revenue
                            |
          +-----------------+-----------------+
          |                 |                 |
     New Revenue      Expansion         Retained
          |            Revenue           Revenue
          |                |                 |
    +-----+-----+    +-----+-----+    +-----+-----+
    |           |    |           |    |           |
  Leads x    Conv  Users x   Upsell  Existing x (1-Churn)
  Rate       Rate   ARPU      Rate    Revenue     Rate

                    Product Health Score
                            |
         +------------------+------------------+
         |                  |                  |
    Engagement          Retention         Satisfaction
         |                  |                  |
    +----+----+       +----+----+       +----+----+
    |         |       |         |       |         |
   DAU/     Time    Day 1    Day 30    NPS    Support
   MAU      in App  Retention Retention       Tickets

## Our North Star Framework

**North Star:** Weekly Active Teams (WAT)

**Input Metrics:**
1. New team signups (acquisition)
2. Teams completing onboarding (activation)
3. Features used per team per week (engagement)
4. Teams inviting new members (virality)
5. Teams on paid plans (monetization)

**Lagging Validation:**
- Revenue growth
- Net retention rate
- Customer lifetime value

Q1 Goals:
- Increase revenue
- Improve engagement
- Reduce churn

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

Sales Pipeline:
  Leading: # of qualified meetings this week
  Lagging: Quarterly revenue

Customer Success:
  Leading: Product usage frequency
  Lagging: Customer churn rate

Engineering:
  Leading: Code review turnaround time
  Lagging: Production incidents

Marketing:
  Leading: Website traffic, MQLs
  Lagging: Customer acquisition cost (CAC)

Leading                                           Lagging
----------------------------------------------------------->

Blog posts    Website     MQLs      SQLs      Deals     Revenue
published  -> traffic  -> generated -> created -> closed -> booked
   |            |           |          |         |         |
   v            v           v          v         v         v
 Actionable  Actionable   Somewhat   Less      Hard      Result
             (SEO, ads)   (content)  control   control

## 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)

Monthly Review:
- Revenue: $485K (missed $500K target)
- Churn: 5.2% (above 5% target)

[Too late to fix - no early warning]

Weekly (Leading):
- Active users: 12,500 → trend down, investigate
- Feature adoption: 68% → below 75%, action needed

Monthly (Lagging):
- Revenue: Validated prediction accuracy
- Churn: Confirms leading indicators correlation

Objective: Qualitative, inspiring goal
+-- Key Result 1: Quantitative measure of progress
+-- Key Result 2: Quantitative measure of progress
+-- Key Result 3: Quantitative measure of progress

[Verb] [metric] from [baseline] to [target] by [deadline]

Examples:
- Increase NPS from 32 to 50
- Reduce time-to-value from 14 days to 3 days
- Achieve 95% feature adoption in first 30 days

## Q1 OKRs

### Objective 1: Become the #1 choice 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
- KR4: Win 5 competitive displacements from [Competitor]

### Objective 2: Build a world-class engineering culture

**Key Results:**
- KR1: Reduce deploy-to-production time from 4 hours to 15 minutes
- KR2: Achieve 90% code coverage on critical paths
- KR3: Zero P0 incidents lasting longer than 30 minutes
- KR4: Engineering satisfaction score reaches 4.5/5

Company OKRs
    |
    v
Department OKRs (aligns to company)
    |
    v
Team OKRs (aligns to department)
    |
    v
Individual OKRs (optional, aligns to team)

Objective: Build a great product

Key Results:
- Ship 10 features
- Write 50 unit tests
- Hold 20 customer interviews

Objective: Become the #1 choice for enterprise teams

Key Results:
- Increase enterprise NPS from 32 to 50
- Reduce time-to-value from 14 days to 3 days
- Achieve 95% feature adoption in first 30 days

Total Time: 3-4 hours

1. OKR Foundations (20 min)
2. Review Company/Team Context (20 min)
3. Objective Brainstorming (45 min)
4. Key Result Definition (60 min)
5. Alignment Check (30 min)
6. Finalization (25 min)

"OKRs help us focus on what matters most and align our efforts.
Today we'll set [N] Objectives with [M] Key Results each.

Key principles:
- Objectives are QUALITATIVE and INSPIRATIONAL
- Key Results are QUANTITATIVE and MEASURABLE
- Aim for 70% achievement (stretch, not sandbagging)
- Focus on outcomes, not outputs"

OBJECTIVE: Qualitative, inspiring, time-bound
├── What do we want to achieve?
├── Why does it matter?
└── Is it ambitious but achievable?

KEY RESULT: Quantitative, measurable, has deadline
├── How will we know we succeeded?
├── Is it specific and unambiguous?
└── Can we track progress?

Company OKRs
    │
    ▼
Department OKRs (aligns to company)
    │
    ▼
Team OKRs (aligns to department)
    │
    ▼
Individual OKRs (optional, aligns to team)

[Verb] [metric] from [baseline] to [target] by [deadline]

Examples:
- Increase NPS from 32 to 50
- Reduce time-to-value from 14 days to 3 days
- Achieve 95% feature adoption in first 30 days

## Objective: [Inspiring statement]

**Key Results:**
1. [Verb] [metric] from [X] to [Y]
   - Baseline: X
   - Target: Y
   - Owner: @name

2. [Verb] [metric] from [X] to [Y]
   - Baseline: X
   - Target: Y
   - Owner: @name

3. [Verb] [metric] from [X] to [Y]
   - Baseline: X
   - Target: Y
   - Owner: @name