Data & Funnel Analytics End-to-end analytics: set up tracking, interpret data, analyze funnels, measure product engagement, validate conversion paths, and calculate ROI. Principle: Track for decisions, not data — every event should inform an action. Analytics Tracking Event Naming Convention Format: object_action in lowercase snake_case. signup_completed | cta_hero_clicked | checkout_started | onboarding_step_completed Rules: Specific over vague ( cta_hero_clicked not button_clicked ), past tense for completed actions, context in properties not event name. Tracking Plan Category Event Key Properties Marketing page_view page_title, page_location, referrer cta_clicked button_text, location, page form_submitted form_type, page signup_completed method, plan Product onboarding_step_completed step_number, step_name feature_used feature_name, context trial_started plan, source purchase_completed plan, value, currency E-commerce product_viewed product_id, category, price product_added_to_cart product_id, price, quantity checkout_started cart_value, items_count Standard Properties User context: user_id, user_type (free/paid/admin), plan_type Attribution: source, medium, campaign, content, term (UTM params) Page: page_title, page_location, content_group PII hygiene: Never send email, name, or phone as event properties. Use hashed user IDs only. GA4 Implementation // gtag.js custom event gtag ( 'event' , 'signup_completed' , { 'method' : 'email' , 'plan' : 'free' , 'user_id' : userId } ) ; // GTM dataLayer dataLayer . push ( { 'event' : 'signup_completed' , 'method' : 'email' , 'plan' : 'free' } ) ; Enhanced Measurement (enable in GA4): page_view, scroll, outbound_click, site_search, video_engagement, file_download. Conversions: Admin → Events → Toggle "Mark as conversion." Counting: once per session (form submit) or every time (purchase). UTM Parameters Convention: utm_source={channel}&utm_medium={cpc|email|organic|social}&utm_campaign={id}&utm_content={variant}&utm_term={keyword} Apply to ALL paid and email links Never use on internal links (breaks session attribution) Lowercase, hyphens not spaces Document in a UTM tracking sheet Privacy & Compliance GDPR/CCPA: Implement consent management, block GA4 until consent granted GA4 data retention: 14 months max (Admin → Data Settings) IP anonymization enabled Analytics Interpretation GA4 Benchmarks Metric Good Warning Poor Action When Poor Avg Time on Page
3 min 1–3 min <1 min Improve content depth Bounce Rate <40% 40–70% 70% Add internal links, improve intro Engagement Rate 60% 30–60% <30% Review content quality Scroll Depth 75% 50–75% <50% Add visual breaks Pages/Session 2.5 1.5–2.5 <1.5 Improve internal linking Google Search Console Benchmarks Metric Good Warning Poor Action When Poor CTR 5% 2–5% <2% Improve title/meta description Avg Position 1–3 4–10 10 Strengthen content, build links Impressions Growing Stable Declining Refresh content Traffic Quality Matrix High Engagement │ ┌──────────────┼──────────────┐ │ HIDDEN GEM │ STAR │ │ Low traffic │ High traffic│ │ → Promote │ → Maintain │ Low ───────┼──────────────┼──────────────┼─── High Traffic │ UNDERPERFORM│ LEAKY │ Traffic │ Low traffic │ High traffic│ │ → Rework │ → Optimize │ └──────────────┼──────────────┘ │ Low Engagement Anomaly Detection Metric Significant Change Alert Level Traffic ±30% WoW HIGH CTR ±1pp WoW MEDIUM Position ±5 positions HIGH Bounce Rate ±10pp WoW MEDIUM Product Analytics North Star Metric The ONE metric that represents customer value: Company North Star Slack Weekly Active Users Airbnb Nights Booked Spotify Time Listening Shopify GMV Criteria: Represents customer value, correlates with revenue, measurable frequently, rallies the team. Key Metrics by Stage Stage Metrics Acquisition Traffic sources, CPC, visitor → signup rate Activation Signup → first core action, time to value, onboarding completion Retention DAU/MAU (stickiness), D1/D7/D30 retention, churn rate Revenue MRR/ARR, ARPU, LTV, LTV:CAC ratio Referral Viral coefficient, referral signups, NPS Retention Benchmarks Timeframe Good Bad D1 60–80% <40% D7 40–60% <10% D30 30–50% <2% Good = flattening curve. Bad = steep drop-off. Dashboard Design Executive: North Star Metric (big number), revenue (MRR/ARR), key trends Product: Active users, feature usage, retention cohorts, funnels Marketing: Traffic sources, conversion rates, CPA, ROI by channel Funnel Analysis Core Workflow Load and merge user journey data Define funnel steps and calculate step-by-step conversion rates Segment by user attributes (device, cohort, plan) Visualize bottlenecks Generate optimization recommendations Common Funnel Types Funnel Steps E-commerce Promotion → Search → Product View → Add to Cart → Purchase SaaS Signup Landing Page → Sign Up → Email Verify → Onboarding Complete Content Article View → Comment → Share → Subscribe Analysis Patterns Bottleneck identification — Steps with highest drop-off rates Segment comparison — Conversion across user groups Temporal analysis — Conversion over time A/B testing — Compare funnel variations See examples/ for Python implementations with Plotly visualizations. Funnel Validation (DotCom Secrets) Score existing funnels against Russell Brunson's framework: Hook → Story → Offer . Scoring Dimensions Dimension Weight What It Measures Hook Strength 2x Stops the scroll, grabs attention Story Connection 1.5x Creates emotional connection and belief Offer Clarity 2x Clear, compelling, irresistible Value Ladder Fit 1x Fits the ascension path Traffic Match 1.5x Matched to traffic temperature Conversion Path 1x Next step obvious and frictionless Rating Scale Score Verdict 85–100 Conversion Machine — Ready to scale 70–84 Strong Funnel — Fix weak points, then scale 55–69 Leaky Funnel — Fix before scaling traffic 40–54 Broken Funnel — Rebuild key components 0–39 Non-Functional — Start over Traffic Temperature Temperature They Know Appropriate Funnel Cold Nothing about you Lead funnel, value-first content Warm Problem + your solution Tripwire, webinar, challenge Hot Ready to buy Sales page, order form, call booking For complete scoring criteria and examples, see references/full-guide.md . ROI Analysis Core Metrics ROI: (Net Profit / Total Investment) × 100% ✅ INVEST: ROI > 100% (realistic case) ⚠️ REVIEW: ROI 50–100% ❌ REJECT: ROI < 50% Break-Even: Investment / Monthly Net Profit ✅ INVEST: Break-even < 50% of realistic target ❌ REJECT: Break-even > 70% Payback Period: Investment / Monthly Net Profit ✅ INVEST: < 12 months ⚠️ REVIEW: 12–24 months ❌ REJECT: > 24 months 3-Scenario Analysis Always model Best / Realistic / Worst: Case Assumptions Revenue Profit ROI Assessment Worst Pessimistic Risk level Realistic Expected Target Best Optimistic Upside Decision rule: If worst-case ROI ≥ 0%, investment is low-risk. Executive Summary Template [Investment] achieves [ROI%] ROI at [conversion/growth rate]. Break-even occurs at [threshold], with payback in [months]. Investment is [recommended/not recommended] because [reason]. For detailed formulas (NPV, LTV, CAC, sensitivity analysis), see references/roi-reference.md . Validation & QA Before Launch Events fire in GA4 DebugView Properties have expected values No duplicate events Conversions marked correctly UTM parameters captured on landing Ongoing Weekly: Check for sudden drops in key events (>20% change = investigate) Monthly: Audit for new pages/features without tracking Quarterly: Full tracking plan review — remove stale events, add missing ones Tools Category Tools Event Tracking Mixpanel, Amplitude, PostHog (open-source) Session Recording FullStory, LogRocket, Hotjar A/B Testing Optimizely, VWO Web Analytics GA4, Google Search Console Tag Management Google Tag Manager