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Analyze KPI root causes

Explain an unexpected metric movement with evidence and next actions.

Difficulty Intermediate
Time horizon 1h

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.

Best for

  • 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.

Contents

    ← All use cases

    Analyze KPI root causes

    Explain an unexpected metric movement with evidence and next actions.

    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.

    Intermediate
    1h

    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.

    Intermediate
    1h

    Related links

    OpenAI Academy: Data science teams Query tabular data

    Best for

    • 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.

    Skills & Plugins

    • Spreadsheets
      Inspect metric exports, calculate cuts, and create supporting charts.
    • Google Drive
      Read metric definitions, dashboards, launch context, and approved source files.
    • Slack
      Check relevant stakeholder context and recent changes.
    • Documents
      Package the analysis as a reviewable brief with sources and caveats.
    Skill Why use it
    Spreadsheets Inspect metric exports, calculate cuts, and create supporting charts.
    Google Drive Read metric definitions, dashboards, launch context, and approved source files.
    Slack Check relevant stakeholder context and recent changes.
    Documents Package the analysis as a reviewable brief with sources and caveats.

    Starter prompt

    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.
    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.

    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.

    1. State the KPI, comparison period, expected direction, and decision the analysis should support.
    2. Attach the dashboard, metric definition, exports, segment cuts, and relevant context.
    3. Ask ChatGPT to inspect data quality and propose the most useful breakdowns.
    4. Run the starter prompt and review confirmed drivers separately from hypotheses.
    5. 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.

    Challenge the root-cause brief. For each confirmed driver or hypothesis, list: - supporting source and calculation - counterevidence or alternative explanation - segment or cohort where it does not hold - data-quality limitation - next check that would increase confidence Revise the recommendation only when the evidence supports it, and keep all changes visible.

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