Product AnalystProduct Health MetricsIntermediateSingle prompt

DAU/MAU Ratio Analysis AI Prompt

Analyze the DAU/MAU ratio (stickiness) for this product and identify improvement opportunities. DAU and MAU data: {{engagement_data}} Product type: {{product_type}} Time period:... Copy this prompt template, run it in your AI tool, and use related prompts to continue the workflow.

Prompt text
Analyze the DAU/MAU ratio (stickiness) for this product and identify improvement opportunities.

DAU and MAU data: {{engagement_data}}
Product type: {{product_type}}
Time period: {{period}}

1. Stickiness calculation:
   - DAU/MAU ratio: daily_active_users / monthly_active_users x 100%
   - Industry benchmarks by product type:
     - Social/messaging: 40-70% (high daily habit)
     - Productivity/SaaS: 20-40%
     - E-commerce: 5-15% (purchase frequency dependent)
     - Gaming: 20-40%
   - How does this product compare to benchmark?

2. Trend analysis:
   - Plot DAU/MAU over the last 12 months
   - Is stickiness improving, declining, or stable?
   - Is DAU growing faster or slower than MAU? (DAU growing faster = improving stickiness)
   - Identify any inflection points and what caused them

3. Stickiness by segment:
   - DAU/MAU for: new users (< 30 days), established users (30-90 days), power users (> 90 days)
   - DAU/MAU by acquisition channel, plan type, company size
   - Which segment has the highest stickiness? What drives it?

4. Usage pattern analysis:
   - What is the distribution of active days per user per month?
   - Are users clustered into daily users, weekly users, and monthly users?
   - What does the 'weekly user' segment use the product for? (May reveal a different use case)

5. Stickiness drivers:
   - Which features correlate most strongly with daily return visits?
   - Do users who complete {{onboarding_action}} have higher stickiness?
   - Is there a usage threshold that separates sticky from non-sticky users?

Return: stickiness metrics, benchmark comparison, trend analysis, segment breakdown, and stickiness driver analysis.

When to use this prompt

Use case 01

Use it when you want to begin product health metrics work without writing the first draft from scratch.

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 covers the main requested outputs, such as Stickiness calculation:, DAU/MAU ratio: daily_active_users / monthly_active_users x 100%, Industry benchmarks by product type:. The final answer should stay clear, actionable, and easy to review inside a product health metrics workflow for 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 DAU/MAU Ratio Analysis 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 intermediate, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

DAU/MAU Ratio Analysis is a single prompt. 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 Full Product Analytics Chain, Product Health Dashboard Design.