Use case 01
Use it when you want to begin product health metrics work without writing the first draft from scratch.
Product AnalystProduct Health MetricsAdvancedChain
Full Product Analytics Chain AI Prompt
Step 1: North Star definition - define or validate the North Star Metric for this product. Decompose it into Level 1 and Level 2 input metrics. Assign owners to each leaf metric... Copy this prompt template, run it in your AI tool, and use related prompts to continue the workflow.
Prompt text
Step 1: North Star definition - define or validate the North Star Metric for this product. Decompose it into Level 1 and Level 2 input metrics. Assign owners to each leaf metric. Step 2: Growth accounting - apply the growth accounting framework to the last 12 months. Compute the quick ratio trend. Diagnose whether this is a new user, retention, or resurrection problem. Step 3: Funnel audit - map the full acquisition-to-activation funnel. Identify the top 2 drop-off points. Segment the funnel by device, channel, and cohort. Step 4: Retention analysis - build the cohort retention matrix. Compute Day 1, Day 7, and Day 30 retention by cohort. Identify whether newer cohorts are improving or declining. Step 5: Feature adoption - for the top 3 features, compute adoption rates and time-to-first-use. Identify which feature has the strongest correlation with 30-day retention. Step 6: User segmentation - segment users into at least 4 behavioral groups (Champions, At-risk, Dormant, New). Size each segment and compute its contribution to revenue or activity. Step 7: Recommendations and roadmap - synthesize findings into a prioritized list of 5 product and analytics recommendations. For each: the problem it addresses, the expected impact, and the measurement plan.
When to use this prompt
Use case 02
Use it when you want a more consistent structure for AI output across projects or datasets.
Use case 03
Use it when you want prompt-driven work to turn into a reusable notebook or repeatable workflow later.
Use case 04
Use it when you want a clear next step into adjacent prompts in Product Health Metrics or the wider Product Analyst library.
What the AI should return
The AI should return a structured result that is directly usable in a product health metrics workflow, with explicit outputs, readable formatting, and enough clarity to support the next step in product analyst work.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Product Health Metrics.
Frequently asked questions
What does the Full Product Analytics Chain prompt do?+
It gives you a structured product health metrics starting point for product analyst work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for product analyst workflows and marked as advanced, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Full Product Analytics Chain is a chain. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are DAU/MAU Ratio Analysis, Product Health Dashboard Design.