Data → Executive Narrative
Turn analysis results into the story executives need: so-what, why, and what next.
Converts raw analysis output — tables, metrics, findings — into an executive-ready narrative: the headline insight, what's driving it, what it means for the business, and the recommended decision. Enforces the 'so what' discipline most data presentations lack.
The prompt
Variables to fill in: {{audience}}{{results}}
You are a senior data analyst presenting to executives. Turn the analysis below into a decision-ready narrative.
Structure:
**Headline** — one sentence: the single most decision-relevant finding, with the number in it.
**What's happening** — 2-3 sentences describing the pattern, in business terms (not "the coefficient" — "customers who X").
**Why (best evidence)** — the most likely driver(s), clearly labeled as established vs. hypothesis. Never present correlation as cause without saying so.
**So what** — what this means in money, users, or risk, with the arithmetic shown.
**Recommendation** — the specific decision or action this supports, plus what we'd need to confirm first if evidence is incomplete.
**Confidence & caveats** — one honest line: data quality, sample, time range.
Rules: no jargon (p-values → "unlikely to be chance"), every claim traceable to the provided data, round numbers for readability ("about 1 in 5", "$120k/yr").
Audience & decision at stake: {{audience}}
Analysis results:
{{results}}How to use
- 1State the actual decision at stake in {{audience}} — 'CFO deciding whether to renew the tool' focuses everything.
- 2Paste real numbers, not your interpretation — the value is the model rebuilding the story from data.
- 3Check the arithmetic in the So What section; it's usually right, but it's your name on the slide.
- 4Use the caveats line verbatim in your deck. Analysts who state limits get believed more, not less.
Examples
Churn analysis for leadership
Input
Audience: exec team deciding onboarding investment. Results: cohort table showing users completing 3+ setup steps churn at 4%/mo vs 11%/mo overall; 38% of signups complete 3+ steps.
Output
**Headline** — Users who complete 3+ setup steps churn at about a third the rate of everyone else (4% vs 11% monthly) — but only 38% get there. **Why** — labeled hypothesis: setup completion likely proxies for intent AND drives value discovery; we can't yet separate the two... **So what** — moving completion from 38% to 50% would retain roughly [arithmetic shown] additional accounts/yr...
Pro tips
- Run it once per audience: the CFO version and the product-team version of the same analysis differ usefully.
Frequently asked questions
Will it overstate causality?+
The prompt explicitly requires labeling established vs. hypothesized drivers and banning silent correlation-as-cause. That constraint, plus your review, keeps the narrative honest — it's the most common failure in human-made decks too.