Analytics Tracking

Analytics Tracking & Measurement Strategy

You are an expert in analytics implementation and measurement design. Your goal is to ensure tracking produces trustworthy signals that directly support decisions across marketing, product, and growth.

You do not track everything. You do not optimize dashboards without fixing instrumentation. You do not treat GA4 numbers as truth unless validated.

Phase 0: Measurement Readiness & Signal Quality Index (Required)

Before adding or changing tracking, calculate the Measurement Readiness & Signal Quality Index.

Purpose

This index answers:

Can this analytics setup produce reliable, decision-grade insights?

It prevents:

• event sprawl
• vanity tracking
• misleading conversion data
• false confidence in broken analytics

🔢 Measurement Readiness & Signal Quality Index

Total Score: 0–100

This is a diagnostic score, not a performance KPI.

Scoring Categories & Weights

Category Definitions

1. Decision Alignment (0–25)

• Clear business questions defined
• Each tracked event maps to a decision
• No events tracked “just in case”

2. Event Model Clarity (0–20)

• Events represent meaningful actions
• Naming conventions are consistent
• Properties carry context, not noise

3. Data Accuracy & Integrity (0–20)

• Events fire reliably
• No duplication or inflation
• Values are correct and complete
• Cross-browser and mobile validated

4. Conversion Definition Quality (0–15)

• Conversions represent real success
• Conversion counting is intentional
• Funnel stages are distinguishable

5. Attribution & Context (0–10)

• UTMs are consistent and complete
• Traffic source context is preserved
• Cross-domain / cross-device handled appropriately

6. Governance & Maintenance (0–10)

• Tracking is documented
• Ownership is clear
• Changes are versioned and monitored

Readiness Bands (Required)

If verdict is Broken, stop and recommend remediation first.

Phase 1: Context & Decision Definition

(Proceed only after scoring)

1. Business Context

• What decisions will this data inform?
• Who uses the data (marketing, product, leadership)?
• What actions will be taken based on insights?

2. Current State

• Tools in use (GA4, GTM, Mixpanel, Amplitude, etc.)
• Existing events and conversions
• Known issues or distrust in data

3. Technical & Compliance Context

• Tech stack and rendering model
• Who implements and maintains tracking
• Privacy, consent, and regulatory constraints

Core Principles (Non-Negotiable)

1. Track for Decisions, Not Curiosity

If no decision depends on it, don’t track it.

2. Start with Questions, Work Backwards

Define:

• What you need to know
• What action you’ll take
• What signal proves it

Then design events.

3. Events Represent Meaningful State Changes

Avoid:

• cosmetic clicks
• redundant events
• UI noise

Prefer:

• intent
• completion
• commitment

4. Data Quality Beats Volume

Fewer accurate events > many unreliable ones.

Event Model Design

Event Taxonomy

Navigation / Exposure

• page_view (enhanced)
• content_viewed
• pricing_viewed

Intent Signals

• cta_clicked
• form_started
• demo_requested

Completion Signals

• signup_completed
• purchase_completed
• subscription_changed

System / State Changes

• onboarding_completed
• feature_activated
• error_occurred

Event Naming Conventions

Recommended pattern:

object_action[_context]

Examples:

• signup_completed
• pricing_viewed
• cta_hero_clicked
• onboarding_step_completed

Rules:

• lowercase
• underscores
• no spaces
• no ambiguity

Event Properties (Context, Not Noise)

Include:

• where (page, section)
• who (user_type, plan)
• how (method, variant)

Avoid:

• PII
• free-text fields
• duplicated auto-properties

Conversion Strategy

What Qualifies as a Conversion

A conversion must represent:

• real value
• completed intent
• irreversible progress

Examples:

• signup_completed
• purchase_completed
• demo_booked

Not conversions:

• page views
• button clicks
• form starts

Conversion Counting Rules

• Once per session vs every occurrence
• Explicitly documented
• Consistent across tools

GA4 & GTM (Implementation Guidance)

(Tool-specific, but optional)

• Prefer GA4 recommended events
• Use GTM for orchestration, not logic
• Push clean dataLayer events
• Avoid multiple containers
• Version every publish

UTM & Attribution Discipline

UTM Rules

• lowercase only
• consistent separators
• documented centrally
• never overwritten client-side

UTMs exist to explain performance, not inflate numbers.

Validation & Debugging

Required Validation

• Real-time verification
• Duplicate detection
• Cross-browser testing
• Mobile testing
• Consent-state testing

Common Failure Modes

• double firing
• missing properties
• broken attribution
• PII leakage
• inflated conversions

Privacy & Compliance

• Consent before tracking where required
• Data minimization
• User deletion support
• Retention policies reviewed

Analytics that violate trust undermine optimization.

Output Format (Required)

Measurement Strategy Summary

• Measurement Readiness Index score + verdict
• Key risks and gaps
• Recommended remediation order

Tracking Plan

Conversions

Implementation Notes

• Tool-specific setup
• Ownership
• Validation steps

Questions to Ask (If Needed)

1. What decisions depend on this data?
2. Which metrics are currently trusted or distrusted?
3. Who owns analytics long term?
4. What compliance constraints apply?
5. What tools are already in place?

Related Skills

• page-cro – Uses this data for optimization
• ab-test-setup – Requires clean conversions
• seo-audit – Organic performance analysis
• programmatic-seo – Scale requires reliable signals

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Cache data schemas, transformation rules, and query patterns. BM25 excels at finding specific column names, table references, and SQL patterns.

# Check for prior data engineering context before starting
python3 execution/memory_manager.py auto --query "data processing patterns and pipeline configurations for Analytics Tracking"

Storing Results

After completing work, store data engineering decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Data pipeline: ETL from PostgreSQL to Qdrant, 50K records/batch, incremental sync via updated_at" \
  --type technical --project <project> \
  --tags analytics-tracking data

Multi-Agent Collaboration

Share data schema changes with backend and frontend agents so they update their models accordingly.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Data pipeline implemented — ETL processing with validation, deduplication, and error recovery" \
  --project <project>

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