GPT-4o
💼 Business
Intermediate
Growth Experiment Designer
Design a rigorous growth experiment with a clear hypothesis, metrics, test design, and decision criteria.
The Prompt
# Growth Experiment Designer You are a growth engineer. Design a rigorous experiment to test the following growth hypothesis. ## Experiment Context Growth lever: [ACQUISITION / ACTIVATION / RETENTION / REVENUE / REFERRAL] Hypothesis: [IF_WE_DO_X_WE_EXPECT_Y_BECAUSE_Z] Primary metric: [METRIC_TO_MOVE] Guardrail metrics: [METRICS_THAT_MUST_NOT_DEGRADE] Available sample size: [DAILY_USERS_OR_SESSIONS] Timeline: [WEEKS_AVAILABLE_FOR_TEST] ## Experiment Design **Control and Treatment** - Control: current behavior (describe exactly) - Treatment: proposed change (describe exactly) **Statistical Parameters** - Minimum detectable effect: [MDE]% - Statistical significance threshold: [95%] - Power: [80%] - Required sample size per group (calculate this) - Estimated test duration given [DAILY_USERS_OR_SESSIONS] **Segmentation** - Which user segment to run this test on - Exclusion criteria **Instrumentation** - Events to track for primary metric - Events to track for guardrail metrics - How to verify tracking is working before launch ## Decision Framework - Win criteria: primary metric improves by [MDE] with p < 0.05 - Loss criteria: primary metric moves less than [MDE] or degrades guardrails - Ship criteria for inconclusive results ## Next Experiment If this experiment wins, what is the natural follow-on test?
📝 Fill in the blanks
Replace these placeholders with your own content:
[ACQUISITION / ACTIVATION / RETENTION / REVENUE / REFERRAL]
[IF_WE_DO_X_WE_EXPECT_Y_BECAUSE_Z]
[METRIC_TO_MOVE]
[METRICS_THAT_MUST_NOT_DEGRADE]
[DAILY_USERS_OR_SESSIONS]
[WEEKS_AVAILABLE_FOR_TEST]
[MDE]
[95%]
[80%]
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