Engagement Analytics Skill
by @abhishekj9621
Use this skill whenever the user needs help with behavioral analytics, engagement tracking, or data collection across any digital touchpoint. Trigger for: we...
clawhub install engagement-analytics-skillπ About This Skill
name: engagement-analytics-tracker description: > Use this skill whenever the user needs help with behavioral analytics, engagement tracking, or data collection across any digital touchpoint. Trigger for: website behavioral analytics (scroll depth, form abandonment, session tracking, GTM setup, GA4 custom events), email engagement tracking (open/click/attribution via Klaviyo, Mailchimp, or custom platforms), social media engagement monitoring (owned and competitor), mobile app analytics (Firebase, Amplitude, Mixpanel, AppsFlyer), user-level engagement scoring, cohort analysis, conversion tracking, event schema design, data layer setup, attribution modeling, or any request like "track user behavior", "set up analytics", "measure engagement", "build an event schema", "track form abandonment", "email attribution", "app retention analysis", "what events should I track?", or "how do I measure X". Always use this skill β do not guess at tracking implementations from memory; patterns and APIs change.
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
checkout_form_abandoned not form_eventData Layer Structure (Web)
window.dataLayer = window.dataLayer || [];
dataLayer.push({
event: 'event_name', // string β 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 Γ 1) +
(Pages per session Γ 2) +
(Scroll 75%+ events Γ 3) +
(CTA clicks Γ 5) +
(Email opens Γ 2) +
(Email clicks Γ 5) +
(App sessions Γ 3) +
(Feature completions Γ 8) +
(Conversions Γ 20)Score tiers:
0β20: Cold (re-engagement candidate)
21β50: Warming (nurture sequence)
51β100: Engaged (sales-ready consideration)
100+: High Value (priority outreach)
Adjust weights based on business model. Recalculate weekly per user.
Privacy & Compliance Baseline
Do Not Track headers and browser privacy modesQuick Implementation Checklist
New Analytics Setup
Existing Analytics Audit
Cross-Channel Attribution Model
When a user touches multiple channels before converting:
Journey: Paid Ad β Email Click β Direct Visit β ConvertedAttribution 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) β 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 β missing events, duplicates, naming inconsistencies]
TOP INSIGHTS THIS PERIOD: [what the data shows]
ACTION ITEMS: [what to fix or investigate]