name: Analyze KPI root causes tagline: Explain an unexpected metric movement with evidence and next actions. summary: Give ChatGPT KPI dashboards, metric definitions, exports, segment cuts, launch context, and stakeholder threads, then ask it to separate confirmed drivers from hypotheses in a source-backed root-cause brief. skills:
-
token: $spreadsheets description: Inspect metric exports, calculate cuts, and create supporting charts.
-
token: google-drive url: https://github.com/openai/plugins/tree/main/plugins/google-drive description: Read metric definitions, dashboards, launch context, and approved source files.
-
token: slack url: https://github.com/openai/plugins/tree/main/plugins/slack description: Check relevant stakeholder context and recent changes.
-
token: $documents description: Package the analysis as a reviewable brief with sources and caveats. bestFor:
-
Product, growth, or operations teams investigating an unexpected KPI movement.
-
Root-cause questions that require segment, cohort, channel, geography, or product cuts.
-
Reviews where confirmed drivers must stay separate from plausible hypotheses. starterPrompt: title: Find the KPI root cause body: >- Explain why [KPI] changed during [time window].
Use the KPI dashboard, metric definition, source exports, segment cuts, launch or campaign context, and stakeholder threads I provide. Break down the movement by relevant segment, cohort, channel, geography, and product surface.
Return a root-cause brief with:
-
charts and material changes
-
confirmed drivers
-
hypotheses to investigate
-
caveats and data-quality issues
-
source links
-
open questions
-
recommended actions
Do not treat correlation as proof, and do not change the source data. suggestedEffort: high relatedLinks:
-
-
label: "OpenAI Academy: Data science teams" url: https://openai.com/academy/codex-for-work/how-data-science-teams-use-codex/
-
label: Query tabular data url: /codex/use-cases/analyze-data-export
Define the metric before explaining movement
Root-cause work starts with a stable definition, comparison window, and source-of-truth data. Give ChatGPT the KPI definition, dashboard, exports, and context around launches or campaigns before asking it to explain the change.
- State the KPI, comparison period, expected direction, and decision the analysis should support.
- Attach the dashboard, metric definition, exports, segment cuts, and relevant context.
- Ask ChatGPT to inspect data quality and propose the most useful breakdowns.
- Run the starter prompt and review confirmed drivers separately from hypotheses.
- Validate the recommended actions with the metric owner before changing a dashboard or process.
Use charts to make the movement inspectable, but do not treat a segment correlation as proof of cause. Keep source links, caveats, and open questions with the brief.
Challenge the root cause
Ask ChatGPT to look for counterevidence and alternative explanations before the brief goes to leadership.