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Data Analysis

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Sales Performance Review

1. Set Context – “We’re in the Sales_Q4 sheet. Analyse A1:F500.”
2. Read Data – Claude calls read_values to inspect the table, extracting metrics like revenue, units sold, and region.
3. Summarise – Claude streams a narrative with KPIs, highlighting trends or anomalies (e.g., “Region West fell 8% vs Q3”).
4. Create Summary Sheet – Approved tools write an aggregated table to a new worksheet using add_sheets and write_values.
5. Highlight Findings – write_format emphasises top/bottom performers; optional chart created for exec-ready visuals.

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Variance Analysis

• Compare actuals vs budget by prompting: “Calculate variance and percentage variance for the selected table.”
• Workflow:
  • write_values inserts variance formulas (e.g., =B2-C2).
  • drag_formula propagates across rows.
  • write_format applies conditional colour scales to highlight outliers.
  • Claude summarises key deviations and suggests next steps.

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Cohort Analysis

• Attach a PDF with cohort definitions and ask Claude to reconcile it with the Excel dataset.
• The assistant uses read_values, cross-references the PDF, and constructs a pivot-style summary table.
• Results include commentary on retention patterns, plus charts if requested.

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Quality Checks

• Request: “Identify rows with missing critical fields and list them.”
• Claude runs read_values, filters nulls, and writes a remediation table on a staging sheet.
• Optionally, it can generate formulas or a macro to flag missing data automatically for future updates.

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Tips for Analysts

• Provide time periods, dimensions, and metrics explicitly so Claude targets the right columns.
• Approve read operations quickly to keep the conversation flowing; scrutinise write operations before committing.
• Use attachments for definitions, MBR decks, or policy docs to ensure recommendations match business rules.