Build and prioritize a growth experiment backlog
A scored list of growth experiments with hypotheses, metrics, and effort, so you know what to test next.
The prompt
Help me build and prioritize a backlog of growth experiments.
Product and business model: [WHAT I SELL, self-serve or sales-led, price point]
North-star or focus metric: [e.g. "trial-to-paid conversion", "weekly active teams"]
Current funnel numbers: [VISITS > SIGNUPS > ACTIVATED > PAID, or similar, with rates]
Ideas we already have: [PASTE LIST, rough is fine]
Traffic/volume per month: [so we know what is testable]
Team capacity: [e.g. "1 engineer half-time, 1 designer, me"]
Steps:
1. Find the biggest leak in the funnel from my numbers and point most experiments there.
2. Add 5-10 new ideas of your own, spread across acquisition, activation, monetization, and retention.
3. Write each as a hypothesis: "Because we saw [evidence], we believe [change] will improve [metric] for [segment]."
4. Score with ICE (Impact, Confidence, Ease, 1-10 each) and explain the Confidence score in one phrase.
5. Flag any experiment my traffic can't support to a clear result in 4 weeks, and suggest a bolder version or a different metric.
Return a table: Rank | Experiment | Hypothesis | Funnel stage | Primary metric | Guardrail metric | I | C | E | Score | Notes.
Then list the top 3 to start this month and what to stop doing to make room.
Use only the funnel numbers I give; mark missing data as [NEED] and label your Impact estimates as judgments, not forecasts.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Marketing & Brand prompts
What to fill in
- WHAT I SELL, self-serve or sales-led, price point
- e.g. "trial-to-paid conversion", "weekly active teams"
- VISITS > SIGNUPS > ACTIVATED > PAID, or similar, with rates
- PASTE LIST, rough is fine
- so we know what is testable
- e.g. "1 engineer half-time, 1 designer, me"
- evidence
- change
- metric
- segment
- NEED
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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