analytics-tracking

Solid

Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data. Use when the user wants to set up, fix, or evaluate analytics tracking (GA4, GTM, product analytics, events, conversions, UTMs). Focuses on measurement strategy, signal quality, and validation — not just firing events.

AI & Automation 81 stars 18 forks Updated today MIT

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Quality Score: 89/100

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100
Frontmatter 20%
70
Documentation 15%
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Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# 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 | Weight | | ----------------------------- | ------- | | Decision Alignment | 25 | | Event Model Clarity | 20 | | Data Accuracy & Integrity | 20 | | Conversion Definition Quality | 15 | | Attribution & Context | 10 | | Governance & Maintenance | 10 | | **Total** | **100** | --- ### Category Definitions #### 1. Decision Alignment (0–25) * Clear business questions defined * Eac...

Details

Author
aAAaqwq
Repository
aAAaqwq/AGI-Super-Team
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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