Product Metrics That Actually Drive Decisions

Note: This is a growing document that I’m continuously updating based on new experiences and learnings.

As a product manager, I’ve learned that the metrics you choose to track fundamentally shape how your team thinks and acts. Too many PMs get caught up in vanity metrics that look good in presentations but don’t drive real business value.

The Hierarchy of Metrics

1. North Star Metrics

These are the single most important metrics that reflect your product’s core value:

Examples by Product Type:

• SaaS Tool: Weekly Active Users (WAU) completing core workflow
• E-commerce: Monthly Gross Merchandise Value (GMV) per customer
• Content Platform: Monthly engaged users (specific engagement definition)

Why they matter: North Star metrics align the entire team around what success looks like.

2. Leading Indicators

Metrics that predict future performance of your North Star:

For a SaaS Product:

• User activation rate (completed onboarding)
• Time to first value (TTFV)
• Feature adoption rate for core workflows

For E-commerce:

• Cart abandonment rate
• Product page engagement time
• Customer support ticket volume

3. Lagging Indicators

Metrics that confirm whether your strategies worked:

• Customer Lifetime Value (CLV)
• Net Promoter Score (NPS)
• Annual Recurring Revenue (ARR)

Framework: The HEART Method

I’ve found Google’s HEART framework particularly useful for product metrics:

Happiness

• What: User satisfaction and delight
• How: NPS surveys, app store ratings, support satisfaction
• Example: “Users rate our onboarding experience 4.5/5 stars”

Engagement

• What: Level of user involvement
• How: Session duration, actions per session, feature usage
• Example: “Power users spend 45+ minutes per session”

Adoption

• What: New users finding value
• How: Activation rates, feature adoption, user growth
• Example: “60% of new users complete core workflow within 7 days”

Retention

• What: Users coming back
• How: Cohort analysis, churn rate, repeat usage
• Example: “85% of users return within 30 days”

Task Success

• What: Efficiency of user workflows
• How: Completion rates, error rates, time to completion
• Example: “95% of checkout flows complete successfully”

Cohort Analysis: The Game Changer

Why Cohort Analysis Matters

Instead of looking at aggregate metrics, cohort analysis shows how different groups of users behave over time.

Example: SaaS Onboarding

Week 1 Cohort (1000 users):
- Day 1: 1000 users (100%)
- Day 7: 650 users (65%)
- Day 30: 400 users (40%)
- Day 90: 300 users (30%)

Week 8 Cohort (1000 users):
- Day 1: 1000 users (100%)
- Day 7: 750 users (75%) ← Improvement!
- Day 30: 550 users (55%) ← Improvement!
- Day 90: TBD

This shows that onboarding improvements are working.

Metrics I Wish I’d Tracked Earlier

1. Feature Stickiness

Formula: DAU/MAU for specific features Why: Tells you which features create habit formation

2. Customer Health Score

Components:

• Product usage (40%)
• Support ticket volume (20%)
• Payment history (20%)
• Engagement with communications (20%)

3. Time to Value (TTV)

Definition: Time from signup to first meaningful outcome Why: Predicts retention better than most other metrics

4. Feature Flag Success Rate

Definition: % of feature flags that improve target metrics Why: Measures your team’s ability to build the right things

Common Metric Pitfalls

1. Vanity Metrics

❌ Page views: Doesn’t indicate value ✅ Engaged sessions: Users actually doing something valuable

2. Metrics Without Context

❌ “We have 50% churn”: Is this good or bad? ✅ “50% monthly churn, down from 65% last quarter”: Shows progress

3. Too Many Metrics

❌ Tracking 50 metrics: Analysis paralysis ✅ 5-7 key metrics: Focus on what matters

4. Ignoring Segmentation

❌ Overall conversion rate: 3% ✅ Conversion by segment:

• New users: 1%
• Returning users: 8%
• Mobile users: 2%
• Desktop users: 5%

Setting Up Your Metrics Stack

Tools I Recommend

1. Analytics: Mixpanel or Amplitude for event tracking
2. Surveys: Typeform or SurveyMonkey for user feedback
3. Dashboards: Grafana or Tableau for visualization
4. A/B Testing: Optimizely or LaunchDarkly

Implementation Strategy

1. Start with North Star: Define your most important metric
2. Add Leading Indicators: 2-3 metrics that predict North Star
3. Instrument Everything: Track user actions, not just page views
4. Create Dashboards: Make metrics visible to the team
5. Review Weekly: Regular metric reviews with the team

Questions I’m Still Exploring

This is the “growing” part - areas where I’m still learning:

1. How do you measure product-market fit quantitatively?

   • Sean Ellis test (40% very disappointed)
   • Retention curves flattening
   • Organic growth rate

2. What’s the right balance between leading and lagging indicators?

   • Still experimenting with ratios
   • Industry seems to vary significantly

3. How do you handle metrics during product pivots?

   • When to abandon old metrics
   • How to establish new baselines

Next Steps for This Document

I plan to add:

• Industry benchmarks by sector
• More cohort analysis examples
• Metrics for different product stages (startup vs. mature)
• Case studies from my recent projects

What metrics have been most valuable for your product decisions? I’d love to hear about your experiences - reach out on LinkedIn or Twitter.