Product & Growth

Deriving growth experiments from data phenomena

Convert business data into testable hypotheses and design growth experiments with indicators and cycles.

Complete prompt

You are a data-driven growth leader. Design a growth experiment based on the following business phenomena.

Product and stage: [product information]
Observed phenomenon: [Data or user feedback]
Target indicator: [Metrics you want to improve]
Available resources: [Team, channel, cycle]

Output:
- 3 possible explanations for phenomena, distinguishing facts from hypotheses
- List of experiments sorted by impact and validation cost
- Complete plan for the first priority experiment: hypothesis, target population, variables, control group, success indicators, guardrail indicators, period, sample requirements
- Next actions corresponding to different experimental results.

Example output

Priority assumption: Users cannot quickly determine whether a template is suitable for their tasks on the details page.
Experiment: Add real finished product preview for 50% of users; the main indicator is template usage rate, and the guardrail indicator is page exit rate.

USAGE GUIDE

When to use it

Convert business data into testable hypotheses and design growth experiments with indicators and cycles. Use it as a structured starting point, then review the result against your original material.

How to fill the variables

product information: Add concrete facts, context, and constraints. Mark unknown details instead of guessing.
Data or user feedback: Add concrete facts, context, and constraints. Mark unknown details instead of guessing.
Metrics you want to improve: Add concrete facts, context, and constraints. Mark unknown details instead of guessing.
Team, channel, cycle: Add concrete facts, context, and constraints. Mark unknown details instead of guessing.

Get better results

Describe the audience and intended decision.
Include source material and hard constraints.
State what the model must not invent.

Before you use the output

Verify facts, numbers, and quotations.
Check that uncertainty is clearly labeled.
Test or review high-impact recommendations.

Editorial note: This template has been structured for practical use. Results vary by model and input; verify important outputs against primary sources.