Operational Data Insight Extractor
Turn pasted operational metrics into evidence-based trends, implications, and prioritized next actions.
Customise & copy
Add the context you have. The prompt updates as you work.
Paste the table, CSV-style text, or metric list exactly as available.
State the reporting period and any comparison period included in the data.
Define non-standard metrics, abbreviations, segments, or calculation rules.
Live prompt
3 fieldsAnalyze the operational data below and extract decision-ready insights. Use only the information provided; do not invent values, causes, benchmarks, or trends. Compare periods or categories when the data supports comparison. If evidence is insufficient, state that plainly. Operational data: {{raw_data}} Timeframe: {{timeframe}} Metric names or definitions: {{metric_definitions}} Return exactly these sections: 1. What's changing: 2–5 concise bullets naming the most important movements. 2. Direction of change: For each relevant metric, label it Increasing, Decreasing, Stable, Mixed, or Insufficient evidence, and cite the supplied values or periods. 3. Business implications: Explain likely effects on revenue, conversion, pipeline, retention, cost, capacity, or operational efficiency. Distinguish observed facts from reasonable inferences. 4. Actions next: Provide 3–5 prioritized actions, each with an owner role, the metric to monitor, and a practical next step. 5. Data limitations: List missing context, inconsistent definitions, or comparisons that cannot be made. Start immediately with “What's changing.” Do not ask follow-up questions, add an introduction, use hype, or reference tools or files.
Proof before you use it
A real example
Tested on gpt-5.6-luna on 2026-09-10
{
"raw_data": "Segment | Q2 2024 pipeline | Q3 2024 pipeline | Q2 win rate | Q3 win rate | Q2 sales cycle | Q3 sales cycle\nSMB | $1.2M | $1.5M | 22% | 19% | 28 days | 34 days\nMid-market | $2.0M | $2.1M | 27% | 28% | 41 days | 40 days\nEnterprise | $3.4M | $3.0M | 31% | 26% | 67 days | 74 days",
"timeframe": "Q3 2024 compared with Q2 2024",
"metric_definitions": "Win rate = closed-won deals divided by closed deals. Sales cycle is the median number of days from opportunity creation to close. Pipeline values are end-of-quarter open pipeline."
}A small ritual that works
How to use it
- 01Paste the available table, CSV-style text, or metric list into Raw operational data.
- 02Enter the reporting period and any comparison period in Timeframe context.
- 03Define non-standard metrics, abbreviations, segments, or calculation rules in Metric names or definitions.
- 04Review the evidence-based changes, implications, actions, and data limitations.
Why it works
The prompt creates a disciplined path from raw operational data to decisions. It limits the analysis to supplied information, requires plain statements when evidence is insufficient, and separates observed facts from reasonable inferences. Its fixed sections move from changes, to metric direction, to business implications, to assigned actions, and finally to data limitations. Requiring an owner role, a monitored metric, and a practical next step makes the action list more operational than a general summary. The defined labels—Increasing, Decreasing, Stable, Mixed, and Insufficient evidence—also provide a consistent way to describe movement without forcing unsupported conclusions.
Where it fails
The prompt cannot establish causes, quantify revenue impact, or connect open pipeline balances directly to closed outcomes when the supplied data lacks deal counts, bookings, cohort alignment, stage information, or causal context. Its business implications therefore remain bounded by the available measures. It also cannot assess retention, capacity, cost, seasonality, or persistent trends unless those dimensions are included in the input.
Customisation tips
Adapt the metric definitions field to specify calculation rules, cohort logic, segment names, and reporting conventions. If the workflow requires different decision owners, replace the generic owner-role expectation with the roles used by the organization. Add required measures such as bookings, opportunity counts, pipeline age, stage distribution, customer retention, or capacity when those decisions matter. For recurring reporting, define the comparison periods and whether metrics should be compared by snapshot, cohort, or completed-period outcome. Keep the section sequence when the goal is to preserve a clear progression from evidence to action.
Watch-outs
- Open pipeline, win rate, and sales-cycle measures may describe different opportunity groups; do not treat them as a directly connected funnel without cohort definitions.
- The prompt asks for likely business effects, but those effects must remain clearly marked as inferences rather than stated facts.
- A short timeframe can describe movement without establishing a durable trend or seasonality.
- Action recommendations may be specific in process but should not imply that the supplied data identifies the root cause.
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