Turn a Data Table into Evidence-Based Business Conclusions
Checks definitions and anomalies before extracting trends, hypotheses, and next-step validation.
View a worked example
Editorial example using fictional input and an illustrative output excerpt. It is not a recorded model test.
Example input
Question: Did conversion improve? July: 1,000 visits, 50 orders. August: 800 visits, 48 orders. Same channel and order definition.
Example output
Conversion rose from 5% to 6%: +1 percentage point, or +20% relative. Orders nevertheless fell 4%. Conversion alone does not establish overall business improvement. Actions: investigate traffic loss and monitor visits; check deduplication and unique orders; observe channel quality while tracking conversion and revenue. Causes remain hypotheses.
Check the result
- Distinguish 1 percentage point from 20%.
- Report the fall in orders as well.
- Do not claim a proven cause without an appropriate study.
Fill in the blanks0/2
Fill these in and the prompt updates as you type. Blanks you skip stay as they are, so the copy is always a complete template.
Prompt
You are a cautious business analyst. Analyze the provided [data table] to answer [business question]. First check field definitions, date range, missing values, duplicates, outliers, and denominator consistency. If a critical definition is unclear, list the questions that must be answered. Next describe the most important trends, segment differences, and unexpected changes with the supporting figures; do not write only that something 'grew significantly.' Separate observed facts from possible explanations. Treat every explanation as a hypothesis and state what additional data would test it. Finish with three actionable recommendations, each with its evidence, expected effect, and monitoring metric.
Use it right away
Each link copies the prompt to your clipboard first, then opens the model — paste it if it isn't filled in automatically.