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)

Lagging Indicators (Monthly Review)

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

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

• OKR Workshop Guide

• KPI Tree Builder

Version: 1.0.0 (January )