| name | product-analyst |
| description | Track user metrics and provide data-driven insights for product decisions. Use when measuring product health, analyzing user behavior, conducting cohort analysis, or optimizing key metrics. Covers acquisition, engagement, retention, revenue metrics, and data-driven decision making. |
Product Analyst
Measure user behavior and product health to inform data-driven decisions.
Core Principle
What gets measured gets improved. Define the right metrics, track them relentlessly, and act on insights quickly.
North Star Metric
The ONE metric that best captures value delivered to users.
Your North Star should:
- ✅ Represent real customer value
- ✅ Correlate with revenue
- ✅ Be measurable frequently (daily/weekly)
- ✅ Rally the entire team around one goal
Examples by Product Type:
Communication:
Slack: Messages Sent (weekly active)
Zoom: Weekly Meeting Minutes
Discord: Active Servers
Marketplace:
Airbnb: Nights Booked
Uber: Completed Rides
Etsy: Gross Merchandise Value (GMV)
Media/Content:
Spotify: Time Listening
Netflix: Hours Watched
Medium: Total Time Reading
SaaS/B2B:
Asana: Weekly Active Teams
Notion: Collaborative Documents
Salesforce: Deals Closed (CRM value)
Social:
Facebook: Daily Active Users (DAU)
Instagram: Posts Shared
Twitter: Tweets per User
How to choose your North Star:
- What action represents core value?
- If users do this more, do they get more value?
- Does this predict revenue?
- Can the entire team influence it?
Key Metrics by Category
Acquisition Metrics
Goal: Get users into the product
Traffic Sources:
- Organic Search: SEO traffic
- Paid Ads: Google Ads, Facebook Ads
- Referral: Word of mouth, links
- Direct: Typed URL, bookmarked
- Social: Twitter, LinkedIn posts
Key Metrics:
- Unique Visitors: Total website visitors
- Sign-ups: Users who created account
- Conversion Rate: Visitors → Sign-ups
- Cost Per Acquisition (CPA): Ad spend / sign-ups
- Source Quality: Which sources convert best?
Targets:
- Visitor → Sign-up: 2-5% (good), 5-10% (excellent)
- CPA: < $50 (B2C), < $200 (B2B), depends on LTV
Activation Metrics
Goal: Get users to "aha moment"
Activation Definition:
- User completes onboarding
- User takes first core action
- User experiences product value
Examples:
Slack: Sent 2,000 messages (team is active)
Dropbox: Added file to folder
Twitter: Followed 30 accounts
Airbnb: Completed first booking
Key Metrics:
- Activation Rate: Sign-ups → Activated
- Time to Activation: How long to aha moment?
- Onboarding Completion: % who finish setup
Targets:
- Activation Rate: >40% (good), >60% (excellent)
- Time to Activation: <24 hours (ideal)
Engagement Metrics
Goal: Keep users coming back
Key Metrics:
- Daily Active Users (DAU)
- Weekly Active Users (WAU)
- Monthly Active Users (MAU)
- DAU/MAU Ratio (Stickiness): How often users return
- Session Frequency: Times per week user logs in
- Session Duration: Time spent per visit
- Feature Adoption: % using each feature
DAU/MAU Stickiness:
Excellent: >40% (Facebook, Slack)
Good: 20-40% (most SaaS)
Needs Work: <20%
Session Frequency Targets:
B2C Social: 5-7 times per week
B2B Tools: 3-5 times per week
E-commerce: 1-2 times per week
Retention Metrics
Goal: Prevent churn
Cohort Retention:
- Day 1: % still active 1 day after sign-up
- Day 7: % still active 7 days after
- Day 30: % still active 30 days after
Good Retention Curves:
Consumer B2C:
- D1: 60-80%
- D7: 40-60%
- D30: 30-50%
- Flattening curve (good!)
Enterprise B2B:
- D1: 80-90%
- D7: 70-80%
- D30: 60-70%
- Very flat curve
Bad Retention:
- D1: 40%
- D7: 10%
- D30: 2%
- Steep drop-off = product-market fit issue
Churn Rate:
- Monthly Churn: % users who stop using each month
- Target: <5% (consumer), <1% (enterprise)
- Churn = Revenue Leak
Net Retention:
- (Starting Users + New - Churned) / Starting Users
- Target: >100% (growth despite churn)
Revenue Metrics
Goal: Monetize effectively
Key Metrics:
- MRR (Monthly Recurring Revenue): Predictable monthly income
- ARR (Annual Recurring Revenue): MRR × 12
- ARPU (Average Revenue Per User): Revenue /
- LTV (Lifetime Value): Total revenue from user over lifetime
- CAC (Customer Acquisition Cost): Sales + marketing / new customers
- LTV:CAC Ratio: Must be > 3:1
- Payback Period: Months to recover CAC
Calculations:
LTV = ARPU × Average Lifetime (months)
Average Lifetime = 1 / Churn Rate
Example:
ARPU: $50/month
Churn: 5% per month
Average Lifetime: 1 / 0.05 = 20 months
LTV: $50 × 20 = $1,000
CAC: $300
LTV:CAC = $1,000 / $300 = 3.3:1 (Good!)
Targets:
- LTV:CAC: >3:1 (minimum), >4:1 (healthy)
- Payback Period: <12 months
- MRR Growth: >10% month-over-month (early stage)
Satisfaction Metrics
Goal: Keep customers happy
NPS (Net Promoter Score):
Question: "How likely are you to recommend us?" (0-10)
- Promoters: 9-10
- Passives: 7-8
- Detractors: 0-6
NPS = % Promoters - % Detractors
Benchmarks:
Excellent: >50
Good: 30-50
Needs Work: <30
CSAT (Customer Satisfaction):
Question: "How satisfied are you?" (1-5)
Target: >4.0 average
CES (Customer Effort Score):
Question: "How easy was it to [task]?" (1-7)
Target: <3.0 (low effort)
Segmentation
Don't treat all users the same. Different cohorts behave differently.
Segment by Engagement:
Power Users (Top 10%):
- Use daily
- High engagement
- Understand product deeply
→ Interview them for feature ideas
Casual Users (Middle 60%):
- Use occasionally
- Basic feature adoption
→ What prevents them from power usage?
At-Risk Users (Bottom 20%):
- Haven't logged in 7+ days
- Low engagement
→ Re-engagement campaign
Churned Users:
- No activity 30+ days
→ Exit survey, understand why
Segment by Acquisition Source:
- Organic vs Paid
- Which source has best retention?
- Which source has best LTV?
Segment by Plan:
- Free vs Paid
- Starter vs Pro vs Enterprise
- Which tier has best retention?
Segment by Cohort (Sign-up Date):
- Week 1 users vs Week 2 users
- Did product changes improve metrics?
Funnel Analysis
Track conversion at each stage:
Sign-up Funnel Example:
1. Land on homepage: 10,000 users (100%)
2. Click "Sign Up": 2,000 users (20%)
3. Fill sign-up form: 1,200 users (12%)
4. Verify email: 800 users (8%)
5. Complete onboarding: 400 users (4%)
Analysis:
Biggest drop-off: Homepage → Sign Up (80% lost)
Fix: Clarify value prop, add social proof, improve CTA
Second drop-off: Form → Email verify (33% lost)
Fix: Simplify form, reduce friction
Optimize biggest drop-offs first for max impact.
Cohort Analysis
Compare user groups over time:
Example: Retention by Sign-up Week
Week 1 Cohort (Jan 1-7):
100 users signed up
- D1: 80 active (80%)
- D7: 40 active (40%)
- D30: 20 active (20%)
Week 2 Cohort (Jan 8-14):
120 users signed up
- D1: 102 active (85%) ← +5% improvement!
- D7: 60 active (50%) ← +10% improvement!
- D30: 36 active (30%) ← +10% improvement!
Insight: Onboarding changes in Week 2 improved retention!
Action: Roll out Week 2 changes to all users.
A/B Testing
Test hypotheses systematically:
1. Form Hypothesis: 'Adding social proof to homepage will increase sign-ups by 10%'
2. Design Experiment:
- Control: Current homepage
- Treatment: Homepage + customer testimonials
- Split: 50/50 traffic
- Primary Metric: Sign-up rate
- Duration: 2 weeks or 1,000 visitors per variant
3. Run Test:
- Don't peek early (wait for significance)
- Monitor for bugs/issues
4. Analyze Results:
Control: 1,000 visitors → 20 sign-ups (2.0%)
Treatment: 1,000 visitors → 25 sign-ups (2.5%)
Lift: +25% relative
P-value: 0.04 (significant at p<0.05)
Decision: WIN - Ship it!
5. Document Learning: 'Social proof increases sign-ups by 25%. Apply to all high-intent pages.'
Minimum Sample Size:
- 100+ conversions per variant minimum
- More is better for small effects
Dashboard Design
Executive Dashboard
Top Metrics (Big Numbers):
- North Star Metric: 12,500 WAU
- MRR: $42,000 (+12% MoM)
- Users: 1,850 (+15% MoM)
Graphs (Trends):
- North Star over time
- Revenue growth
- User acquisition
Alerts:
- Churn spike: +20% this week ⚠️
- Trial conversion down: 10% → 8% ⚠️
Product Dashboard
Engagement:
- DAU: 3,200
- WAU: 8,500
- MAU: 15,000
- Stickiness (DAU/MAU): 21%
Feature Usage:
- Feature A: 80% adoption
- Feature B: 45% adoption
- Feature C: 12% adoption (low!)
Retention:
- D1: 75%
- D7: 50%
- D30: 35%
Funnels:
- Sign-up → Activation: 45%
- Trial → Paid: 12%
Marketing Dashboard
Acquisition:
- Visitors: 50,000
- Sign-ups: 2,000 (4% conversion)
- Activated: 800 (40% activation)
By Source:
- Organic: 20,000 visitors, 5% conversion
- Paid: 15,000 visitors, 3% conversion
- Referral: 10,000 visitors, 6% conversion (best!)
Cost Efficiency:
- CPA: $150
- LTV: $600
- LTV:CAC: 4:1 (healthy!)
Tools & Software
Event Tracking:
- Mixpanel (best for product analytics)
- Amplitude (great alternative)
- PostHog (open-source)
- Google Analytics 4 (free, basic)
Session Recording:
- FullStory (see user sessions)
- LogRocket (debugging + analytics)
- Hotjar (heatmaps + recordings)
A/B Testing:
- Optimizely
- VWO
- Google Optimize (free, basic)
- LaunchDarkly (feature flags + testing)
Data Warehouse:
- Snowflake
- BigQuery
- Redshift
Visualization:
- Tableau
- Looker
- Metabase (open-source)
Reporting Cadence
Daily:
- Check North Star Metric
- Monitor error rates
- Review yesterday's experiments
Weekly:
- Funnel analysis
- Cohort retention
- Feature adoption
- Share insights with team
Monthly:
- MRR/ARR review
- LTV:CAC ratio
- Churn analysis
- Send NPS survey
Quarterly:
- Deep dive on user segments
- Competitive benchmarking
- Strategic planning with leadership
Quick Start Checklist
Common Pitfalls
❌ Vanity metrics: Tracking metrics that look good but don't predict success (e.g., page views)
❌ Too many metrics: Focus on 3-5 key metrics, not 50
❌ No North Star: Team pulls in different directions
❌ Ignoring segments: Averages hide important patterns
❌ Analysis paralysis: Measure, learn, act quickly
❌ Not acting on data: Data without action is worthless
Summary
Great product analysis:
- ✅ One North Star Metric everyone tracks
- ✅ AARRR framework (Acquisition, Activation, Retention, Revenue, Referral)
- ✅ Cohort analysis over time
- ✅ Segmentation (not all users are the same)
- ✅ Regular A/B testing
- ✅ Share insights widely with team
- ✅ Act on data quickly
