Data Analysis
Funnel Drop-off Experiments
Funnel Drop-off Experiments analyzes supplied material for patterns, evidence, and decision-ready findings with clear constraints and an explicit output.
The prompt
Funnel Drop-off Experiments guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when exploring data and explaining what the results support and you want a response that is easier to evaluate and act on.
text prompt
You are a senior product analyst.
Task:
Given this funnel identify the biggest drop-offs and propose 5 experiments per drop-off: {{funnelData}}.
Inputs:
- Funnel data: {{funnelData}}
Output requirements:
- Return JSON only. Do not wrap in Markdown or code fences.
- Do not add keys beyond the schema.
- Populate clarifying_questions if any critical input is missing or ambiguous, but still produce a best-effort deliverable using assumptions.
- Add a short, realistic next_steps list that would be performed after using this deliverable.
- Include deeper reasoning artifacts as structured fields (assumptions, risks, tradeoffs) while keeping the final deliverable usable.
JSON schema (example values only, keep the same keys):
{
"title": "Funnel Drop-off Experiments",
"deliverable_type": "Prioritized experiment list",
"clarifying_questions": [],
"assumptions": [],
"deliverable_markdown": "",
"quality_checks": [
"Every metric includes definition, formula, data source, cadence, and caveats",
"Metrics are grouped logically and avoid duplicates",
"Assumptions are explicitly listed"
],
"next_steps": []
}
Tune the prompt, not the plumbing.
Every control comes from this prompt’s content schema. Changes stay in your browser and update instantly.
Customized prompt1186 characters
You are a senior product analyst.
Task:
Given this funnel identify the biggest drop-offs and propose 5 experiments per drop-off: Paste the relevant source material and context here..
Inputs:
- Funnel data: Paste the relevant source material and context here.
Output requirements:
- Return JSON only. Do not wrap in Markdown or code fences.
- Do not add keys beyond the schema.
- Populate clarifying_questions if any critical input is missing or ambiguous, but still produce a best-effort deliverable using assumptions.
- Add a short, realistic next_steps list that would be performed after using this deliverable.
- Include deeper reasoning artifacts as structured fields (assumptions, risks, tradeoffs) while keeping the final deliverable usable.
JSON schema (example values only, keep the same keys):
{
"title": "Funnel Drop-off Experiments",
"deliverable_type": "Prioritized experiment list",
"clarifying_questions": [],
"assumptions": [],
"deliverable_markdown": "",
"quality_checks": [
"Every metric includes definition, formula, data source, cadence, and caveats",
"Metrics are grouped logically and avoid duplicates",
"Assumptions are explicitly listed"
],
"next_steps": []
}
Useful structure, room to move.
Funnel Drop-off Experiments guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when exploring data and explaining what the results support and you want a response that is easier to evaluate and act on.
The prompt establishes the job first, then supplies concrete decisions a model can act on. The variables preserve that structure while letting you change the subject, context, or output.
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