--- name: majiayu000/product-analytics source: https://app.decimal.ai/s/majiayu000-product-analytics@1/SKILL.md source_sha256: 34c59ab01675 --- # Product Analytics ## Core Principles - **Metrics over vanity** — Focus on actionable metrics tied to business outcomes - **Data-driven decisions** — Hypothesize, measure, learn, iterate - **User-centric measurement** — Track behavior, not just pageviews - **Statistical rigor** — Understand significance, avoid false positives - **Privacy-first** — Respect user data, comply with GDPR/CCPA - **North Star focus** — Align all teams around one key metric --- ## Hard Rules (Must Follow) > These rules are mandatory. Violating them means the skill is not working correctly. ### No PII in Events **Events must NEVER contain personally identifiable information.** ```javascript // ❌ FORBIDDEN: PII in event properties track('user_signed_up', { email: 'user@example.com', // PII! name: 'John Doe', // PII! phone: '+1234567890', // PII! ip_address: '192.168.1.1', // PII! credit_card: '4111...', // NEVER! }); // ✅ REQUIRED: Anonymized/hashed identifiers only track('user_signed_up', { user_id: hash('user@example.com'), // Hashed plan: 'pro', source: 'organic', country: 'US', // Broad location OK }); // Masking utilities const maskEmail = (email) => { const [name, domain] = email.split('@'); return `${name[0]}***@${domain}`; }; ``` ### Object_Action Event Naming **All event names must follow the object_action snake_case format.** ```javascript // ❌ FORBIDDEN: Inconsistent naming track('signup'); // No object track('newProject'); // camelCase track('Upload File'); // Spaces and PascalCase track('user-created'); // kebab-case track('BUTTON_CLICKED'); // SCREAMING_CASE // ✅ REQUIRED: object_action snake_case track('user_signed_up'); track('project_created'); track('file_uploaded'); track('payment_completed'); track('checkout_started'); ``` ### Actionable Metrics Only **Track metrics that drive decisions, not vanity metrics.** ```javascript // ❌ FORBIDDEN: Vanity metrics without context track('page_viewed'); // No insight track('button_clicked'); // Too generic track('app_opened'); // Doesn't indicate value // ✅ REQUIRED: Actionable metrics tied to outcomes track('feature_activated', { feature: 'dark_mode', time_to_activation_hours: 2.5, user_segment: 'power_user', }); track('checkout_completed', { order_value: 99.99, items_count: 3, payment_method: 'credit_card', coupon_applied: true, }); ``` ### Statistical Rigor for Experiments **A/B tests must have proper sample size and significance thresholds.** ```javascript // ❌ FORBIDDEN: Drawing conclusions too early // "After 100 users, variant B has 5% higher conversion!" // This is not statistically significant. // ✅ REQUIRED: Proper experiment setup const experimentConfig = { name: 'new_checkout_flow', hypothesis: 'New flow increases conversion by 10%', // Statistical requirements significance_level: 0.05, // 95% confidence power: 0.80, // 80% power minimum_detectable_effect: 0.10, // 10% lift // Calculated sample size sample_size_per_variant: 3842, // Guardrails max_duration_days: 14, stop_if_degradation: -0.05, // Stop if 5% worse }; ``` --- ## Quick Reference ### When to Use What | Scenario | Framework/Tool | Key Metric | |----------|---------------|------------| | Overall product health | North Star Metric | Time spent listening (Spotify), Nights booked (Airbnb) | | Growth optimization | AARRR (Pirate Metrics) | Conversion rates per stage | | Feature validation | A/B Testing | Statistical significance (p < 0.05) | | User engagement | Cohort Analysis | Day 1/7/30 retention rates | | Conversion optimization | Funnel Analysis | Drop-off rates per step | | Feature impact | Attribution Modeling | Multi-touch attribution | | Experiment success | Statistical Testing | Power, significance, effect size | --- ## North Star Metric ### Definition A North Star Metric is the **one metric** that best captures the core value your product delivers to customers. When this metric grows sustainably, your business succeeds. ### Characteristics of Good NSMs ``` ✓ Captures product value delivery ✓ Correlates with revenue/growth ✓ Measurable and trackable ✓ Movable by product/engineering ✓ Understandable by entire org ✓ Leading (not lagging) indicator ``` ### Examples by Company | Company | North Star Metric | Why It Works | |---------|------------------|--------------| | **Spotify** | Time Spent Listening | Core value = music enjoyment | | **Airbnb** | Nights Booked | Revenue driver + value delivered | | **Slack** | Daily Active Teams | Engagement = product stickiness | | **Facebook** | Monthly Active Users | Network effect foundation | | **Amplitude** | Weekly Learning Users | Value = analytics insights | | **Dropbox** | Active Users Sharing Files | Core product behavior | ### NSM Framework ``` North Star Metric ↓ ┌──────┴──────┬──────────┬──────────┐ │ │ │ │ Input 1 Input 2 Input 3 Input 4 (Supporting metrics that drive NSM) Example: Spotify NSM: Time Spent Listening ├── Daily Active Users ├── Playlists Created ├── Songs Added to Library └── Share/Social Actions ``` ### How to Define Your NSM 1. **Identify core value proposition** - What job does your product do for users? - When do users get "aha!" moment? 2. **Find the metric that represents this value** - Transaction completed? (e.g., Nights Booked) - Time engaged? (e.g., Time Listening) - Content created? (e.g., Messages Sent) 3. **Validate it correlates with business success** - Does NSM increase → revenue increases? - Can product changes move this metric? 4. **Define supporting input metrics** - What user behaviors drive NSM? - Break into 3-5 key inputs --- ## AARRR Framework (Pirate Metrics) ### Overview The AARRR framework tracks the customer lifecycle across five stages: ``` ACQUISITION → ACTIVATION → RETENTION → REFERRAL → REVENUE ``` ### Stage Definitions #### 1. Acquisition **When users discover your product** **Key Questions:** - Where do users come from? - Which channels have best quality users? - What's the cost per acquisition (CPA)? **Metrics:** ``` • Website visitors • App installs • Sign-ups per channel • Cost per acquisition (CPA) • Channel conversion rates ``` **Example Events:** ```javascript // Landing page view track('page_viewed', { page: 'landing', utm_source: 'google', utm_medium: 'cpc', utm_campaign: 'brand_search' }); // Sign-up started track('signup_started', { source: 'homepage_cta' }); ``` #### 2. Activation **When users experience core product value** **Key Questions:** - What's the "aha!" moment? - How long to first value? - What % reach activation? **Metrics:** ``` • Time to first action • Activation rate (% completing key action) • Setup completion rate • Feature adoption rate ``` **Example "Aha!" Moments:** ``` Slack: Send 2,000 messages in team Twitter: Follow 30 users Dropbox: Upload first file LinkedIn: Connect with 5 people ``` **Example Events:** ```javascript // Activation milestone track('activated', { user_id: 'usr_123', activation_action: 'first_project_created', time_to_activation_hours: 2.5 }); ``` #### 3. Retention **When users keep coming back** **Key Questions:** - What's Day 1/7/30 retention? - Which cohorts retain best? - What drives churn? **Metrics:** ``` • Day 1/7/30 retention rate • Weekly/Monthly active users (WAU/MAU) • Churn rate • Usage frequency • Feature stickiness (DAU/MAU) ``` **Retention Calculation:** ``` Day X Retention = Users returning on Day X / Total users in cohort Example: Cohort: 1000 users signed up Jan 1 Day 7: 300 returned Day 7 Retention = 300/1000 = 30% ``` **Example Events:** ```javascript // Daily engagement track('session_started', { user_id: 'usr_123', session_count: 42, days_since_signup: 15 }); ``` #### 4. Referral **When users recommend your product** **Key Questions:** - What's the viral coefficient (K-factor)? - Which users refer most? - What referral incentives work? **Metrics:** ``` • Viral coefficient (K-factor) • Referral rate (% users referring) • Invites sent per user • Invite conversion rate • Net Promoter Score (NPS) ``` **Viral Coefficient:** ``` K = (% users who refer) × (avg invites per user) × (invite conversion rate) Example: K = 0.20 × 5 × 0.30 = 0.30 K > 1: Viral growth (each user brings >1 new user) K < 1: Need paid acquisition ``` **Example Events:** ```javascript // Referral actions track('invite_sent', { user_id: 'usr_123', channel: 'email', recipients: 3 }); track('referral_converted', { referrer_id: 'usr_123', new_user_id: 'usr_456', channel: 'email' }); ``` #### 5. Revenue **When users generate business value** **Key Questions:** - What's customer lifetime value (LTV)? - What's LTV:CAC ratio? - Which segments monetize best? **Metrics:** ``` • Monthly Recurring Revenue (MRR) • Average Revenue Per User (ARPU) • Customer Lifetime Value (LTV) • LTV:CAC ratio • Conversion to paid • Revenue churn ``` **LTV Calculation:** ``` LTV = ARPU × Gross Margin / Churn Rate Example: ARPU: $50/month Gross Margin: 80% Churn: 5%/month LTV = $50 × 0.80 / 0.05 = $800 Healthy LTV:CAC ratio: 3:1 or higher ``` **Example Events:** ```javascript // Revenue events track('subscription_started', { user_id: 'usr_123', plan: 'pro', mrr: 29.99, billing_cycle: 'monthly' }); track('upgrade_completed', { user_id: 'usr_123', from_plan: 'basic', to_plan: 'pro', mrr_change: 20.00 }); ``` ### AARRR Metrics Dashboard ```markdown ## Acquisition - Total visitors: 50,000 - Sign-ups: 2,500 (5% conversion) - Top channels: Organic (40%), Paid (30%), Referral (20%) ## Activation - Activated users: 1,750 (70% of sign-ups) - Time to activation: 3.2 hours (median) - Activation funnel drop-off: 30% at setup step 2 ## Retention - Day 1: 60% - Day 7: 35% - Day 30: 20% - Churn: 5%/month ## Referral - K-factor: 0.4 - Users referring: 15% - Invites per user: 4.2 - Invite conversion: 25% ## Revenue - MRR: $125,000 - ARPU: $50 - LTV: $800 - LTV:CAC: 4:1 - Conversion to paid: 25% ``` --- ## Extended Reference Detailed material starting at `## Key Metrics & Formulas` has been moved to [`reference/extended.md`](reference/extended.md) to keep this skill concise. Load that reference when the task requires the moved examples, command catalogs, checklists, platform details, or implementation templates.