data-and-funnel-analytics

name

data-and-funnel-analytics

description

Analytics tracking, interpretation, funnel analysis, product metrics, and ROI measurement. Use when setting up GA4/GTM tracking, interpreting analytics data, analyzing conversion funnels, calculating ROI, or measuring product engagement. Triggers on "analytics," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "tracking plan," "funnel analysis," "conversion rates," "user flow," "cohort analysis," "retention," "product metrics," "North Star metric," "ROI," "break-even," "payback period," "investment analysis," "validate my funnel," "why isn't my funnel converting," or "executive financial report." For A/B test setup, see ab-test-setup.

Data & Funnel Analytics

Workspace Context

Read bootstrap context before asking questions: strategy/brand.md for brand, audience, offer, channels, tools, constraints, and metrics; about/me.md for personal voice; content/ideas.md and content/calendar.md for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md, and route durable learnings back to strategy/brand.md, about/me.md, or content/ideas.md.

Operating Contract

This skill is self-contained for its frontmatter scope: use its local instructions, references, scripts, and assets as the playbook; ask only for missing task-specific inputs; hand off to adjacent skills instead of expanding scope; and return an actionable artifact, decision, plan, draft, or diagnostic.

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.

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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

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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

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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

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Funnel Analysis

Core Workflow

1. Load and merge user journey data
2. Define funnel steps and calculate step-by-step conversion rates
3. Segment by user attributes (device, cohort, plan)
4. Visualize bottlenecks
5. 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.

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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.

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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.

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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

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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

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Related Skills

• ab-test-setup — A/B test measurement and setup
• seo-and-aeo-strategy — Measuring SEO/AEO performance
• conversion-rate-optimization — Optimizing conversion after funnel analysis
• executive-dashboard-generator — Building dashboards from analytics data