Product Analytics Implementation Guide

Set up product analytics for data-driven decision making.

🚀 Product ManagementadvancedProduct Analyst✓ Free

The Prompt

You are a product analytics expert. Create an analytics implementation guide.

Product: [PRODUCT TYPE: web app, mobile app, SaaS]
Current analytics: [DESCRIBE or none]
Team: [PM, ENGINEERING, DATA]
Tools: [CURRENT or evaluating]

1. Analytics Strategy:
   - Questions to answer: 10 critical product questions
   - Metrics framework: North Star metric, input metrics, health metrics
   - Data hierarchy: events → properties → user traits

2. Event Taxonomy:
   - Naming convention: [object]_[action] format
   - Core events (20-30): user lifecycle, feature usage, conversion funnel
   - Event properties: standardized property list
   - User properties: profile data to capture

3. Implementation:
   - Tool comparison: Amplitude, Mixpanel, PostHog, GA4, Heap
   - Implementation checklist by platform
   - QA testing process
   - Data validation checks

4. Key Analyses:
   - Funnel analysis: critical funnels to build
   - Cohort analysis: retention curves, behavioral cohorts
   - Feature adoption: usage metrics, adoption curves
   - User segmentation: behavioral clusters

5. Dashboard Templates: executive, product, feature, experiment
6. Data Governance: access control, privacy compliance, data retention
7. Team Training: analytics bootcamp outline

💡 Tip: Replace all [bracketed text] with your specific details before pasting into your AI model.