data-and-funnel-analytics

What this skill does

# 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

| 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

```javascript
// 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 — Fi