Product Analytics Implementation Guide
Create a comprehensive implementation guide for setting up product analytics for [product/feature]. Include: 1) Key user actions and events to track, 2) User properties and traits to capture, 3) Recommended analytics platform with justification, 4) Tracking plan with naming conventions, 5) Implementation instructions for developers, 6) Data validation process, 7) Dashboard setup guidelines, 8) Team training recommendations, 9) Common analytics implementation pitfalls and how to avoid them, and 10) Maintenance and governance plan.
How to Use This Prompt
- Copy the prompt using the button above
- Replace placeholders in [brackets] with your specific details
- Paste into your AI assistant (ChatGPT, Claude, Gemini, etc.)
- Iterate as needed - ask follow-up questions to refine the output
Why This Prompt Works
This prompt bridges the gap between PM requirements and engineering execution by covering the full analytics lifecycle -- from defining what to track through validation, training, and governance. The ten-part structure ensures nothing falls through the cracks, producing a document that developers can implement directly while avoiding common pitfalls like inconsistent naming or missing event properties.
When to Use This Prompt
- Launching a new feature and need to ensure proper instrumentation before it ships
- Migrating to a new analytics platform and need a clean tracking plan from the start
- Inheriting a product with messy or incomplete analytics and need to rebuild tracking systematically
Tips for Better Results
- Specify your tech stack (React, iOS, Android) so the implementation instructions match your codebase
- Include your current analytics tool and any you are evaluating so the AI can compare platform capabilities
- Describe 3-5 key user flows you care about most to get the most relevant event tracking recommendations