data-and-funnel-analytics

manojbajaj95/claude-gtm-plugin · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/manojbajaj95/claude-gtm-plugin --skill data-and-funnel-analytics
0 commentsdiscussion
summary

End-to-end analytics: set up tracking, interpret data, analyze funnels, measure product engagement, validate conversion paths, and calculate ROI.

skill.md

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

  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.


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

How to use data-and-funnel-analytics on Cursor

AI-first code editor with Composer

1

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
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/manojbajaj95/claude-gtm-plugin --skill data-and-funnel-analytics

The skills CLI fetches data-and-funnel-analytics from GitHub repository manojbajaj95/claude-gtm-plugin and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/data-and-funnel-analytics

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

GET_STARTED →

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. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 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

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.555 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.

showing 1-10 of 55

1 / 6