data-and-funnel-analytics▌
manojbajaj95/claude-gtm-plugin · updated Apr 8, 2026
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End-to-end analytics: set up tracking, interpret data, analyze funnels, measure product engagement, validate conversion paths, and calculate ROI.
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 |
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
How to use data-and-funnel-analytics on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add data-and-funnel-analytics
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches data-and-funnel-analytics from GitHub repository manojbajaj95/claude-gtm-plugin and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate data-and-funnel-analytics. Access the skill through slash commands (e.g., /data-and-funnel-analytics) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★55 reviews- ★★★★★Pratham Ware· Dec 28, 2024
I recommend data-and-funnel-analytics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kabir Sharma· Dec 28, 2024
Keeps context tight: data-and-funnel-analytics is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Naina Garcia· Dec 24, 2024
Solid pick for teams standardizing on skills: data-and-funnel-analytics is focused, and the summary matches what you get after install.
- ★★★★★Alexander Chen· Dec 8, 2024
Registry listing for data-and-funnel-analytics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Amelia Patel· Nov 27, 2024
Solid pick for teams standardizing on skills: data-and-funnel-analytics is focused, and the summary matches what you get after install.
- ★★★★★Kabir Kapoor· Nov 19, 2024
data-and-funnel-analytics is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Noor Li· Nov 15, 2024
Registry listing for data-and-funnel-analytics matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Alexander Brown· Nov 3, 2024
data-and-funnel-analytics reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Alexander Sanchez· Oct 22, 2024
I recommend data-and-funnel-analytics for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Diya Okafor· Oct 18, 2024
We added data-and-funnel-analytics from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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