Analytics Documentation

Why this matters

Clear analytics documentation is the bridge between product ideas and trustworthy data. As a Product Analyst, you will define what to track, how it should behave, and how teams will use it. Good docs prevent mismatched events, broken funnels, and painful data cleanups. Typical tasks include:

• Writing tracking plans for new features and experiments.
• Defining event names, triggers, and properties with types and examples.
• Coordinating with engineers on implementation tickets and QA steps.
• Versioning changes and maintaining a change log to keep reports stable.

Who this is for and prerequisites

Who this is for

• Product Analysts who design or audit event tracking.
• Product Managers and Engineers collaborating on instrumentation.
• Data Analysts/Scientists consuming product data for insights.

Prerequisites

• Basic understanding of product funnels and user journeys.
• Familiarity with events, properties, user IDs, and sessions.
• Ability to read simple JSON-like specs.

Concept explained simply

Analytics documentation is a single source of truth that explains what events exist, when they fire, what properties they carry, who owns them, and how changes are governed. It’s like a blueprint plus an instruction manual for your product data.

Mental model

Think of your tracking plan as a contract between product, engineering, and analytics. The contract states: “When a user does X in Y context, send event Z with these properties, in this format, every time.” Then it adds rules for identity, QA, and versioning so the contract stays reliable as the product evolves.

Core components of analytics documentation

• Event taxonomy and naming: Clear, consistent, human-readable (e.g., "Signup Started", "Checkout Completed").
• Event definition: Purpose, trigger conditions, platform scope (web/iOS/Android/backend), and sample user stories.
• Properties schema: Name, type (string/number/boolean/object), allowed values, examples, whether required/optional, and PII/sensitivity flags.
• Identity strategy: How user_id, anonymous_id/device_id, and session are defined; when and how IDs merge.
• Governance: Naming conventions, deprecation policy, ownership, review/approval workflow.
• Versioning and change log: Semantic versioning for events/properties; documented rationale and migration notes.
• Source-to-destination mapping: SDK/screen locations, backend services, and data warehouse tables/columns.
• QA plan: Test cases, acceptance criteria, edge cases, and how to validate in staging/production.
• Status: Proposed, In Development, QA, Released; link to implementation ticket IDs (referenced by name only).

Worked examples

Example 1: Event definition

{
  "event": "Checkout Completed",
  "properties": {
    "order_id": "ORD-93421",
    "total_amount": 89.99,
    "currency": "USD",
    "items_count": 3,
    "coupon_code": "SUMMER10",
    "payment_method": "card"
  },
  "context": {"user_id": "u_123"}
}

Example 2: Versioning and change log

Example 3: Instrumentation ticket text

How to create a clear tracking plan

1. List user actions: Map the journey. Choose high-signal events first (start, progress, success, failure).
2. Define triggers precisely: Tie to business logic, not UI clicks (e.g., after API success, not on button tap).
3. Specify properties: Types, allowed values, required/optional, examples, and PII flags.
4. Set identity rules: When to send user_id vs anonymous_id, and merging behavior.
5. Add QA cases: One-liners engineers and QA can run in staging.
6. Governance: Owners, review steps, versioning, and a change log entry.

Exercises

Try the exercise below. You can compare with the solution. Tip: keep triggers tied to business logic, and include QA cases.

1. Exercise 1 (mirrors ex1): Draft an event spec for "Save to Wishlist" covering: purpose, trigger, platforms, properties (with types, required flags, and examples), identity expectations, and QA checks. Add one enum and one numeric constraint.

Common mistakes and self-check

• Click-level triggers instead of business outcomes. Self-check: Does the event fire only after success?
• Free-text enums leading to inconsistent values. Self-check: Are allowed values listed and enforced?
• Missing identity rules causing broken user stitching. Self-check: Are user_id and anonymous_id behavior documented?
• Unversioned changes breaking reports. Self-check: Did you add a change log entry and migration note?
• PII leakage in properties. Self-check: Did you mark sensitive fields and avoid raw PII?

Practical projects

• Create a mini tracking plan (5–7 events) for an onboarding flow, including properties and QA steps.
• Audit an existing event: propose a minor version update and write the change log and migration note.
• Write an instrumentation ticket for a new event with acceptance criteria and staging validation steps.

Learning path

• Start here: Analytics documentation foundations (this page).
• Next: QA for event tracking and debugging techniques.
• Then: Data modeling for events in the warehouse and dashboard spec documentation.

Next steps

• Polish your Exercise 1 spec and store it in your tracking plan.
• Take the quick test to check your understanding.

Mini challenge

Pick one event in your product that has a vague name. Rename it using Verb + Object, define clear trigger conditions, and add two properties with explicit types and allowed values. Write one QA step that would catch a duplicate firing bug.

Quick test note

Take the Quick Test below to validate your knowledge. Everyone can take the test for free. Only logged-in users get saved progress.