OKR & KPI Patterns

Frameworks for defining goals, measuring success, and building metrics-driven organizations.

OKR Framework

Objectives and Key Results align teams around ambitious goals with measurable outcomes.

OKR Structure

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

Writing Good Objectives

Characteristic	Good	Bad
Qualitative	"Delight enterprise customers"	"Increase NPS to 50"
Inspiring	"Become the go-to platform"	"Ship 10 features"
Time-bound	Implied quarterly	Vague timeline
Ambitious	Stretch goal (70% achievable)	Sandbagged (100% easy)

Writing Good Key Results

Characteristic	Good	Bad
Quantitative	"Reduce churn from 8% to 4%"	"Improve retention"
Measurable	"Ship to 10,000 beta users"	"Launch beta"
Outcome-focused	"Increase conversion by 20%"	"Add 5 features"
Leading indicators	"Weekly active users reach 50K"	"Revenue hits $1M" (lagging)

OKR Example

## 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

Leading vs. Lagging Indicators

Understanding the difference is crucial for effective measurement.

Definitions

Type	Definition	Characteristics
Leading	Predictive, can be directly influenced	Real-time feedback, actionable
Lagging	Results of past actions	Confirms outcomes, hard to change

Examples by Domain

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)

The Leading-Lagging Chain

Leading                                           Lagging
─────────────────────────────────────────────────────────►

Blog posts    Website     MQLs      SQLs      Deals     Revenue
published  →  traffic  →  generated → created → closed → booked
   │            │           │          │         │         │
   ▼            ▼           ▼          ▼         ▼         ▼
 Actionable  Actionable   Somewhat   Less      Hard      Result
             (SEO, ads)   (content)  control   control

Using Both Effectively

## Balanced Metrics Dashboard

### Leading Indicators (Weekly Review)
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Active users (DAU) | 12,500 | 15,000 | 🟡 |
| Feature adoption rate | 68% | 75% | 🟡 |
| Support ticket volume | 142 | <100 | 🔴 |
| NPS responses collected | 89 | 100 | 🟢 |

### Lagging Indicators (Monthly Review)
| Metric | Current | Target | Status |
|--------|---------|--------|--------|
| Monthly revenue | $485K | $500K | 🟡 |
| Customer churn | 5.2% | <5% | 🟡 |
| NPS score | 42 | 50 | 🟢 |
| CAC payback months | 14 | 12 | 🔴 |

KPI Trees

Hierarchical breakdown of metrics showing cause-effect relationships.

Revenue KPI Tree

                         Revenue
                            │
          ┌─────────────────┼─────────────────┐
          │                 │                 │
     New Revenue      Expansion         Retained
          │            Revenue           Revenue
          │                │                 │
    ┌─────┴─────┐    ┌─────┴─────┐    ┌─────┴─────┐
    │           │    │           │    │           │
  Leads ×    Conv  Users ×   Upsell  Existing × (1-Churn)
  Rate       Rate   ARPU      Rate    Revenue     Rate

Product Health KPI Tree

                    Product Health Score
                            │
         ┌──────────────────┼──────────────────┐
         │                  │                  │
    Engagement          Retention         Satisfaction
         │                  │                  │
    ┌────┴────┐       ┌────┴────┐       ┌────┴────┐
    │         │       │         │       │         │
   DAU/     Time    Day 1    Day 30    NPS    Support
   MAU      in App  Retention Retention       Tickets

North Star Metric

One metric that captures core value delivery.

Examples by Business Type

Business Type	North Star Metric	Why
SaaS	Weekly Active Users	Indicates ongoing value
Marketplace	Gross Merchandise Value	Captures both sides
Media	Time spent reading	Engagement = value
E-commerce	Purchase frequency	Repeat = satisfied
Fintech	Assets under management	Trust + usage

North Star + Input Metrics

## 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

Metric Definition Template

## Metric: [Name]

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

### Formula

Metric = Numerator / Denominator


### 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]

Common Pitfalls

Pitfall	Mitigation
Vanity metrics	Focus on metrics that drive decisions
Too many KPIs	Limit to 5-7 per team
Gaming metrics	Pair metrics that balance each other
Lagging only	Include leading indicators for early signals
No baselines	Establish current state before setting targets
Static goals	Review and adjust quarterly

Best Practices

OKRs for goals, KPIs for health: Use together, not interchangeably
Leading indicator focus: Key Results should be leading indicators
Cascade with autonomy: Align outcomes, let teams choose their path
Regular calibration: Weekly check-ins on leading, monthly on lagging
AI-assisted insights: Use AI to detect anomalies and suggest actions

Related Skills

product-strategy-frameworks - Strategic context for metrics
business-case-analysis - Financial metrics and ROI
prioritization-frameworks - Using metrics to prioritize

References

Version: 1.0.0 (January )

okr-kpi-patterns