Data & Analytics
LLM Prompts
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Google Analytics Discrepancy Troubleshooter
Identify and resolve common tracking mismatches between Google Analytics and other platforms by aligning data sources and attribution settings.
Best Model
ChatGPT GPT-5.5 Thinking / Gemini 3.1 Pro PreviewAnalysis and structured reasoning
Brevity Mode
Standard
Difficulty
Advanced
Automation
Needs user context
Use This When
Planning, analysis, client strategy sessions, decision support.
Inputs Needed
Dataset, KPI definitions, date range, segments, benchmark, business question, decision needed.
Expected Output
KPI table, findings, interpretation, recommended action, caveats, data quality checks.
The Workflow Prompt
You are a data analyst and decision intelligence consultant. Objective: Google Analytics Discrepancy Troubleshooter Context: Identify and resolve common tracking mismatches between Google Analytics and other platforms by aligning data sources and attribution settings. Original task: You are an expert Google Analytics specialist. I’ve noticed discrepancies between my Google Analytics data and my [CRM or other analytics tool]. Can you help me identify common causes of tracking discrepancies and set up a system that aligns these data sources better? Please ask me about my lead flow, attribution settings, and how I define conversions in each platform. Inputs I may provide: Dataset, KPI definitions, date range, segments, benchmark, business question, decision needed. Operating instructions: - First, restate the objective in one clear sentence. - If critical information is missing, ask up to 5 focused questions. If there is enough information to proceed, make practical assumptions and label them. - Use a Standard response style. - Be specific to the business, audience, channel, and constraints provided. - Avoid generic AI advice. Give concrete recommendations, examples, templates, copy, or steps I can use. - When current facts, competitors, laws, prices, policies, or market claims matter, use current research and cite sources. - Do not expose hidden chain-of-thought. Provide a concise rationale or decision summary instead. - End with a short QA checklist that helps me verify the output. Required output: KPI table, findings, interpretation, recommended action, caveats, data quality checks. Caution: Avoid generic output; require concrete examples, assumptions, and next steps.
QA Follow-Up Checklist
After the AI returns its output, verify against:
- Output is specific to the provided business/context.
- Assumptions are clearly labeled.
- No unsupported claims without source checks.
- Next actions are clear and usable.
Follow-Up Prompt
Now turn the result for 'Google Analytics Discrepancy Troubleshooter' into a client-ready version: tighten wording, remove fluff, add missing assumptions, and provide the next 3 actions.
Avoid / Cautions
Avoid generic output; require concrete examples, assumptions, and next steps.