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use-cases/business-impact-readout.md 2026-07-27 18:59 UTC to 2026-07-28 23:01 UTC

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name: Measure business impact tagline: Turn experiment or launch results into a scale, change, or stop recommendation. summary: Give ChatGPT an experiment or launch plan, success metrics, cohort data, dashboard exports, customer signals, and launch notes, then ask it to quantify lift, check guardrails, explain segments, and draft a sourced impact readout. skills:

  • token: $spreadsheets description: Calculate lift, guardrails, segment differences, and supporting tables or charts.

  • token: google-drive url: https://github.com/openai/plugins/tree/main/plugins/google-drive description: Read experiment plans, dashboard exports, cohort tables, and launch notes.

  • token: $documents description: Create a stakeholder-ready impact readout with methodology notes and caveats. bestFor:

  • Experiments, launches, or initiatives that need a clear scale, adjust, or stop recommendation.

  • Teams comparing lift across cohorts or segments while checking guardrail metrics.

  • Readouts that need methodology, caveats, and confirmed results separated from interpretation. starterPrompt: title: Write a business impact readout body: >- Measure whether [initiative, experiment, or launch] improved [target outcome].

    Use the experiment or launch plan, success metrics, relevant dashboards, cohort or assignment data, customer signals, and launch notes I provide. Quantify lift or movement, check guardrail metrics, inspect segment differences, and explain whether the team should scale, change, or stop the initiative.

    Return a business impact readout with charts, methodology notes, caveats, source links, and a clear recommendation. Separate confirmed results from interpretation and flag any missing inputs. suggestedEffort: high relatedLinks:

  • label: "OpenAI Academy: Data science teams" url: https://openai.com/academy/codex-for-work/how-data-science-teams-use-codex/

  • label: Analyze datasets and ship reports url: /codex/use-cases/datasets-and-reports

For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending .md to the page URL.

Start with the decision the readout must support

An impact readout should connect the result to a decision, such as scale, adjust, or stop. Provide the experiment or launch plan, success metrics, cohorts, guardrails, and customer context so ChatGPT can show what the data supports.

  1. Name the initiative, target outcome, evaluation window, and decision owner.
  2. Attach the plan, metric definitions, assignment or cohort data, dashboards, and launch notes.
  3. Ask ChatGPT to validate inputs and explain the analysis method before writing the recommendation.
  4. Run the starter prompt and check lift, guardrails, and segment differences.
  5. Review methodology, caveats, and missing data with the analyst or experiment owner.

Keep measured results separate from interpretation. If the design or data cannot support a causal claim, ask ChatGPT to narrow the language rather than fill the gap.

Audit the recommendation

Before sharing the readout, ask for a compact evidence audit focused on the recommendation and its most important numbers.

Prompt:

Audit the business impact readout.

Check:

- every material number against its source
- the lift calculation and comparison group
- guardrail metrics
- segment differences and sample gaps
- methodology limitations
- claims that go beyond the data
- whether scale, change, or stop is actually supported

Return corrections and open questions without changing the source data.