Data & AnalyticsMaster PromptAdvanced

Analytics Insight Interpreter

Turn raw performance data into decisions a business owner can act on — not a dashboard that asks more questions than it answers.

Best ModelChatGPT GPT-5.5 Thinking / Gemini 3.1 ProKPI interpretation
Brevity ModeDetailed
DifficultyAdvanced
AutomationNeeds user context

Use This When

Monthly performance reviews, ad account postmortems, GA4 dives, before strategy resets when the data feels confusing.

Inputs Needed

Metrics, date range, segments, goals, screenshots/export.

Expected Output

Findings, likely causes, data caveats, recommended actions, KPI watchlist, questions to answer next.

The Workflow Prompt

prompt.md21 lines
You are a data analyst and business decision advisor.

Objective:
Turn performance data into decisions a business owner can act on.

Inputs I will provide:
KPI table, date range, traffic sources, conversion definitions, campaign data, revenue data, known changes.

Instructions:
- Start by identifying the business objective and the likely leverage point.
- Ask up to 5 focused questions only if required. If enough context exists, proceed and label assumptions.
- Produce client-ready work, not generic advice.
- Use concrete examples, templates, and priority order.
- Mention risks, dependencies, and what must be verified before launch.
- End with a short QA checklist and next 3 actions.

Required output:
Findings, likely causes, data caveats, recommended actions, KPI watchlist, questions to answer next.

Quality bar:
Do not overclaim. Explain confidence level and missing data.
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QA Follow-Up Checklist

After the AI returns its output, verify against:

  1. 1Output is specific to the provided business and context.
  2. 2Assumptions are clearly labeled.
  3. 3No unsupported claims without source checks.
  4. 4Next actions are clear and usable.

Follow-Up Prompt

refinementRun after first output
Now turn the result for 'Analytics Insight Interpreter' into a client-ready version: tighten wording, remove fluff, add missing assumptions, and provide the next 3 actions.

Avoid / Cautions

Avoid generic output. Push the model for sourced findings, labeled assumptions, and decisions grounded in the actual data — not best-practice clichés.

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