# Analytics Tracking

> Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.

- Skill: `ranbot-ai/analytics-tracking` (Agent Skill)
- Install (CLI): `npx skillmds add ranbot-ai/analytics-tracking`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ranbot-ai/analytics-tracking/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: ranbot-ai (https://skillmd.com/u/ranbot-ai)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ranbot-ai/analytics-tracking

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# 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 Evidence and Optional Review Rubric

Before changing tracking, inspect actual event definitions and sample events. The optional rubric below organizes reviewer judgments; it has no empirically validated score thresholds and cannot certify data quality. Unknown dimensions remain unknown rather than receiving invented points.

### Purpose

This index answers:

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

Use it to identify possible:

* 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
* 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

---

### Illustrative planning bands (not validation gates)

| Score  | Verdict               | Interpretation                    |
| ------ | --------------------- | --------------------------------- |
| 85–100 | **Measurement-Ready** | Review whether observed evidence supports the intended decision   |
| 70–84  | **Usable with Gaps**  | Fix issues before major decisions |
| 55–69  | **Unreliable**        | Data cannot be trusted yet        |
| <55    | **Broken**            | Do not act on this data           |

Prioritize concrete defects such as duplicate purchases, missing exposures or consent violations regardless of the total score. A high score must never override a failed reconciliation.

---

## Phase 1: Context & Decision Definition

(Start from the product decision and available evidence)

### 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 spac