engagement-analytics-tracker

# Engagement Analytics Tracker Skill

A comprehensive skill for designing, implementing, and interpreting behavioral analytics
across four touchpoint layers: website, email, social, and mobile app.

---

## Four Tracking Modules

| Module | Reference File | Use When |
|--------|----------------|----------|
| Website Behavioral Analytics | `references/website-analytics.md` | GTM, GA4, scroll/form/session tracking |
| Email Engagement Tracker | `references/email-analytics.md` | Klaviyo, Mailchimp, open/click/attribution |
| Social Media Engagement | `references/social-analytics.md` | Owned + competitor social tracking |
| Mobile App Analytics | `references/mobile-analytics.md` | Firebase, Amplitude, Mixpanel, AppsFlyer |

**Load strategy:** Load only the relevant module(s) based on the user's question. For full
analytics stack questions ("build me a complete analytics system"), load all four.

---

## Universal Data Principles

These apply across ALL four modules:

### Event Naming Convention (Use Everywhere)
```
object_action
# Examples:
page_viewed          button_clicked       form_abandoned
video_played         product_viewed       email_opened
session_started      feature_used         purchase_completed
```
- Always lowercase with underscores
- Object first, then action
- Be specific: `checkout_form_abandoned` not `form_event`
- Keep consistent across all platforms — the same action has the same name everywhere

### Data Layer Structure (Web)
```javascript
window.dataLayer = window.dataLayer || [];
dataLayer.push({
  event: 'event_name',           // string — always required
  user_id: 'u_abc123',           // hashed or anonymized
  session_id: 'ses_xyz',
  timestamp: new Date().toISOString(),
  page_path: window.location.pathname,
  // event-specific properties below:
  element_id: 'hero_cta',
  element_text: 'Start Free Trial',
});
```

### Engagement Scoring Formula
A composite score usable across web, email, and app:

```
Engagement Score = 
  (Sessions × 1) +
  (Pages per session × 2) +
  (Scroll 75%+ events × 3) +
  (CTA clicks × 5) +
  (Email opens × 2) +
  (Email clicks × 5) +
  (App sessions × 3) +
  (Feature completions × 8) +
  (Conversions × 20)

Score tiers:
  0–20:   Cold (re-engagement candidate)
  21–50:  Warming (nurture sequence)
  51–100: Engaged (sales-ready consideration)
  100+:   High Value (priority outreach)
```

Adjust weights based on business model. Recalculate weekly per user.

### Privacy & Compliance Baseline
- Never collect raw PII in event properties — hash emails/IDs before sending to any platform
- Implement consent gating: fire tracking tags only after user consents (GDPR)
- Use server-side tagging (GTM Server-Side) for sensitive data flows
- Respect `Do Not Track` headers and browser privacy modes
- Apple ATT opt-in required for IDFA on iOS — design attribution without assuming access
- CCPA: provide opt-out mechanism; do not sell behavioral data without consent

---

## Quick Implementation Checklist

### New Analytics Setup
- [ ] Define tracking plan: events, properties, naming convention — before touching any tool
- [ ] Set up GTM container (web) or SDK (mobile)
- [ ] Implement dataLayer or SDK event calls
- [ ] Configure GA4 or destination analytics platform
- [ ] Validate all events in debug/preview mode before going live
- [ ] Set up consent management (CMP) gating
- [ ] Create dashboards for key metrics
- [ ] Schedule regular data quality audits

### Existing Analytics Audit
- [ ] Are events named consistently? (check for duplicates with different names)
- [ ] Is user_id passed and consistent across sessions and platforms?
- [ ] Are conversion events firing correctly? (test end-to-end)
- [ ] Is there data loss from consent mode, ad blockers, or iOS ATT?
- [ ] Are email UTM parameters correctly attributed in GA4?
- [ ] Are mobile sessions merging correctly with web sessions (cross-device)?

---

## Cross-Channel Attribution Model

When a user touches multiple channels before converting:

```
Journey: Paid Ad → Email Click → Direct Visit → Converted

Attribution options:
  Last-click:     Direct gets 100% credit (most common, least accurate)
  First-click:    Paid Ad gets 100% credit
  Linear:         All 3 channels get 33% each
  Time-decay:     Direct > Email > Paid Ad (recency-weighted)
  Data-driven:    ML model (GA4 DDA) — most accurate, needs volume
```

**Recommended:** Use GA4 Data-Driven Attribution (DDA) when you have 500+ conversions/month.
Below that volume, use Linear to avoid bias toward any single channel.

Track cross-channel with UTM parameters on all non-direct traffic:
```
?utm_source=klaviyo&utm_medium=email&utm_campaign=may_reengagement&utm_content=cta_button
```

---

## Output Templates

### Event Schema Definition
```
Event Name: [object_action]
Trigger: [when exactly does this fire?]
Properties:
  - property_name (type): description, example value
  - property_name (type): ...
Platform: [GTM / Firebase / Klaviyo / etc.]
Destination: [GA4 / BigQuery / Amplitude / etc.]
Privacy: [PII risk? How handled?]
```

### Analytics Health Report
```
DATE: [date]
COVERAGE: [% of key user actions being tracked]
DATA QUALITY: [issues found — missing events, duplicates, naming inconsistencies]
TOP INSIGHTS THIS PERIOD: [what the data shows]
ACTION ITEMS: [what to fix or investigate]
```