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name: product-analytics description: > Design product analytics — event taxonomy, tracking plan, metric definitions, dashboard requirements, and insight workflows. TRIGGER when: user says /product-analytics or asks about product instrumentation and usage tracking. argument-hint: "[product or feature to instrument]" user-invocable: true

Product Analytics Design

Process

Step 1: Define Analytics Goals

Question	Analytics Needed
Are users activating?	Activation funnel events
Are users engaged?	Session, feature usage events
Are users retaining?	Return frequency, churn events
Are users expanding?	Upsell, feature adoption events
Where do users struggle?	Error, rage-click, drop-off events

Step 2: Design Event Taxonomy

Convention	Format	Example
Object_Action	noun_verb (past tense)	project_created, report_exported
Properties	snake_case, consistent types	plan_type: "pro", item_count: 5
Naming rules	No abbreviations, consistent casing	subscription_upgraded not sub_up

Step 3: Create Tracking Plan

Event	Trigger	Properties	Priority
page_viewed	Page load	page_name, referrer	Must
button_clicked	CTA click	button_name, location	Must
feature_used	Feature activation	feature_name, duration	Must
error_occurred	Error displayed	error_type, error_message	Must
search_performed	Search submitted	query, results_count	Should

Step 4: Define Metrics

Metric	Definition	Formula
DAU/MAU	Daily/Monthly active users	Unique users with session
Activation rate	Users completing setup	Activated / Signed up
Feature adoption	Users using feature X	Feature users / MAU
Retention (D7)	Return after 7 days	Active D7 / Cohort size

Step 5: Design Dashboards

Dashboard	Audience	Metrics
Executive	Leadership	MAU, retention, revenue
Product	PM/Design	Feature adoption, funnels
Growth	Marketing	Acquisition, activation
Engineering	Dev team	Errors, performance

Step 6: Establish Insight Workflow

1. Weekly metrics review (automated report)
2. Anomaly investigation (dig into unusual changes)
3. Hypothesis formation (why did metric change?)
4. Experiment design (A/B test to validate)
5. Decision and iteration

Output Format

## Analytics Plan: [Product]
### Event Taxonomy: [Naming convention and key events]
### Tracking Plan: [Event table]
### Metrics: [Definitions and targets]
### Dashboards: [Requirements by audience]

Quality Checklist

• Event naming is consistent and documented
• All key user actions have tracking events
• Metrics have clear definitions and formulas
• Dashboard requirements match stakeholder needs
• Privacy and consent handled properly

Edge Cases

• For mobile apps, track offline events with batched upload
• For multi-platform, ensure consistent event names across platforms
• If GDPR applies, implement consent management before tracking