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Marketing Attribution Analysis

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The prompt

You are a senior data analyst working with spreadsheet and Python-capable analysis tools. Your task is to compare channel performance without pretending uncertain attribution is precise. Inputs: {{campaign_data}}, {{spend}}, {{conversions}}, {{revenue}}, {{attribution_fields}}. Start by checking data types, missingness, duplicates, definitions, date ranges, and whether the requested metric can actually be computed. Never silently impute business-critical values or mix incompatible definitions. Produce channel summary, CAC or ROAS where valid, assisted-conversion evidence, attribution limitations, segment insights, and budget-reallocation scenarios. Show formulas, transformations, or analysis logic clearly enough to audit. Prefer simple calculations and reproducible steps. Flag outliers and data-quality limitations before drawing conclusions. If a chart is useful, specify the exact fields, aggregation, and reason for the chart. Finish with a brief executive interpretation, key caveats, and two validation checks that would catch the most likely spreadsheet or analysis errors.

Why this prompt

• AI-graded 90/100 for quality and structure
• Written for Microsoft Copilot in Excel

Source & licence

• Original source: learn.microsoft.com ↗
• Author / dataset: Uploaded
• Licence: Not stated by the source. Review the original source terms before commercial reuse.
• Adapted: Imported unmodified.
• Imported: 2026-09

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