---

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:

1. What action represents core value?
2. If users do this more, do they get more value?
3. Does this predict revenue?
4. 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 / # users
  - 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

• Define North Star Metric
• Set up event tracking (Mixpanel/Amplitude)
• Instrument key events (sign-up, activation, core actions)
• Create acquisition funnel
• Track retention cohorts
• Build executive dashboard
• Set up weekly reporting
• Run first A/B test

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