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Data And Funnel Analytics

  • 279 installs
  • 80 repo stars
  • Updated May 18, 2026
  • manojbajaj95/claude-gtm-plugin

data-and-funnel-analytics is a Claude GTM plugin skill that maps funnel stages, measures drop-offs and conversion rates, and segments users so developers running go-to-market analytics can pinpoint leaks and prioritize p

About

data-and-funnel-analytics is a go-to-market analytics skill from manojbajaj95/claude-gtm-plugin that helps developers and product teams diagnose funnel performance. The skill structures GTM funnel stages, quantifies drop-offs and conversion rates between steps, and segments users to isolate where prospects stall. Claude uses the analysis to recommend prioritized experiments that improve pipeline throughput. Developers reach for data-and-funnel-analytics when SaaS metrics show conversion cliffs between signup, activation, or purchase stages and they need a structured diagnostic frame rather than ad hoc spreadsheet review.

  • Multi-stage funnel definition and benchmarking
  • Drop-off and stage conversion rate analysis
  • GTM-oriented user and cohort segmentation
  • Leak diagnosis with experiment recommendations
  • Pipeline metrics aligned to go-to-market motions

Data And Funnel Analytics by the numbers

  • 279 all-time installs (skills.sh)
  • +4 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #251 of 853 Sales & Marketing skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs279
repo stars80
Last updatedMay 18, 2026
Repositorymanojbajaj95/claude-gtm-plugin

How do you find drop-offs in a SaaS conversion funnel?

Map GTM funnel stages, measure drop-offs and conversion rates, and segment users so Claude can pinpoint leaks and recommend prioritized experiments to improve pipeline performance.

Who is it for?

Developers and product engineers supporting SaaS GTM who need funnel stage metrics and experiment priorities from pipeline data.

Skip if: Developers implementing product code, infrastructure monitoring, or application error tracking should use engineering analytics or observability skills instead.

When should I use this skill?

The user asks to analyze funnel conversion, segment users by stage drop-off, or prioritize GTM experiments from pipeline metrics.

What you get

Funnel stage map, conversion and drop-off metrics, user segments, and a prioritized experiment backlog.

Files

SKILL.mdMarkdownGitHub ↗

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.

---

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

CategoryEventKey Properties
Marketingpage_viewpage_title, page_location, referrer
cta_clickedbutton_text, location, page
form_submittedform_type, page
signup_completedmethod, plan
Productonboarding_step_completedstep_number, step_name
feature_usedfeature_name, context
trial_startedplan, source
purchase_completedplan, value, currency
E-commerceproduct_viewedproduct_id, category, price
product_added_to_cartproduct_id, price, quantity
checkout_startedcart_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

MetricGoodWarningPoorAction When Poor
Avg Time on Page>3 min1–3 min<1 minImprove 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>2.51.5–2.5<1.5Improve internal linking

Google Search Console Benchmarks

MetricGoodWarningPoorAction When Poor
CTR>5%2–5%<2%Improve title/meta description
Avg Position1–34–10>10Strengthen content, build links
ImpressionsGrowingStableDecliningRefresh 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

MetricSignificant ChangeAlert Level
Traffic±30% WoWHIGH
CTR±1pp WoWMEDIUM
Position±5 positionsHIGH
Bounce Rate±10pp WoWMEDIUM

---

Product Analytics

North Star Metric

The ONE metric that represents customer value:

CompanyNorth Star
SlackWeekly Active Users
AirbnbNights Booked
SpotifyTime Listening
ShopifyGMV

Criteria: Represents customer value, correlates with revenue, measurable frequently, rallies the team.

Key Metrics by Stage

StageMetrics
AcquisitionTraffic sources, CPC, visitor → signup rate
ActivationSignup → first core action, time to value, onboarding completion
RetentionDAU/MAU (stickiness), D1/D7/D30 retention, churn rate
RevenueMRR/ARR, ARPU, LTV, LTV:CAC ratio
ReferralViral coefficient, referral signups, NPS

Retention Benchmarks

TimeframeGoodBad
D160–80%<40%
D740–60%<10%
D3030–50%<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

FunnelSteps
E-commercePromotion → Search → Product View → Add to Cart → Purchase
SaaS SignupLanding Page → Sign Up → Email Verify → Onboarding Complete
ContentArticle 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

DimensionWeightWhat It Measures
Hook Strength2xStops the scroll, grabs attention
Story Connection1.5xCreates emotional connection and belief
Offer Clarity2xClear, compelling, irresistible
Value Ladder Fit1xFits the ascension path
Traffic Match1.5xMatched to traffic temperature
Conversion Path1xNext step obvious and frictionless

Rating Scale

ScoreVerdict
85–100Conversion Machine — Ready to scale
70–84Strong Funnel — Fix weak points, then scale
55–69Leaky Funnel — Fix before scaling traffic
40–54Broken Funnel — Rebuild key components
0–39Non-Functional — Start over

Traffic Temperature

TemperatureThey KnowAppropriate Funnel
ColdNothing about youLead funnel, value-first content
WarmProblem + your solutionTripwire, webinar, challenge
HotReady to buySales 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:

CaseAssumptionsRevenueProfitROIAssessment
WorstPessimisticRisk level
RealisticExpectedTarget
BestOptimisticUpside

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

CategoryTools
Event TrackingMixpanel, Amplitude, PostHog (open-source)
Session RecordingFullStory, LogRocket, Hotjar
A/B TestingOptimizely, VWO
Web AnalyticsGA4, Google Search Console
Tag ManagementGoogle 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

Related skills

FAQ

What metrics does data-and-funnel-analytics produce?

data-and-funnel-analytics produces a mapped GTM funnel with stage-level conversion and drop-off rates, user segments highlighting leak points, and a prioritized list of experiments to improve pipeline performance. Output is diagnostic analytics rather than code changes.

Who should use data-and-funnel-analytics?

data-and-funnel-analytics suits developers and product teams analyzing SaaS go-to-market funnels when signup or activation rates stall. The skill frames leaks and suggests experiments instead of replacing a full product analytics warehouse.

Sales & Marketinglifecycledistribution

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