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100 Best Gemini Prompts for Excel Analysis

A practical prompt library of 100 Gemini prompts for Excel Analysis to help analysts clean data, build dashboards, and derive insights directly in Excel.

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Best For

Excel analysts and data professionals

Prompt Use Cases

  • Data cleaning in Excel
  • Pivot table analysis
  • Dashboard creation in Excel
  • Forecasting and trend analysis in Excel
  • Validation and auditing of Excel models
  • Data reconciliation across sheets

Introduction

This page is a practical prompt library for Excel Analysis, created to help professionals leverage Google Gemini prompts to drive data cleaning, analysis, and dashboarding directly in Excel. It’s designed for analysts, data scientists, and business users who want repeatable, worksheet-based workflows without custom tooling.

The prompts below are tailored to Excel tasks like data cleaning, pivot analysis, time-series forecasting, data validation, and dashboard construction. Use them to guide Gemini in producing actionable Excel outputs that you can directly implement in your workbooks.

Direct Answer

The best Gemini prompts for Excel Analysis are a curated, copyable set of 100 prompts that specify role, task, context placeholders, output formats, and constraints so you can run end-to-end Excel analysis workflows with Gemini without inventing context.

How to Use These Gemini Prompts

  • Replace placeholders like [dataset], [sheet], [columns], and [scenario] with your actual workbook names and column headers before running each prompt.
  • Add concrete constraints (e.g., date formats, currency, or units) to ensure consistent results across runs.
  • Request outputs in a specific format (e.g., a table, chart-ready data, or a summary log) and specify where to place results in the workbook.
  • Verify outputs by cross-checking with a small manual sample and ensure reproducible steps for future updates.
  • When in doubt, ask Gemini to provide a step-by-step plan before delivering the final dataset or chart.

100 Best Gemini Prompts for Excel Analysis

  1. Prompt 1 Role: Excel Analysis Specialist. Task: Clean and standardize the dataset in [dataset], on sheet [sheet], focusing on date and numeric fields. Context: Input contains inconsistent date formats in [date_column], empty values in [cols_missing], and numeric fields stored as text in [num_cols]. Output: a cleaned dataset in [format] with a new summary column [summary_col] and a log of changes. Constraints: preserve original rows, document all changes, reproducible steps, and use only Excel functions.
  2. Prompt 2 Role: Excel Analysis Specialist. Task: Identify and remove duplicates while preserving the most complete record in [dataset], sheet [sheet]. Context: Duplicates found across columns [dup_cols]; maintain unique keys in [key_cols]. Output: a deduplicated dataset in [format] with a report of duplicates removed. Constraints: keep first occurrence, log original row numbers.
  3. Prompt 3 Role: Excel Analysis Specialist. Task: Pivot analysis to summarize sales by region and month. Context: Data in [dataset], sheet [sheet], columns [region_col], [month_col], [sales_col]. Output: a pivot table in [format] with totals and a 3-month moving average column. Constraints: show grand totals and include a filter for [region_filter].
  4. Prompt 4 Role: Excel Analysis Specialist. Task: Build a moving average forecast for [metric] over [periods] in [dataset], sheet [sheet]. Context: Time series in [date_col] with frequency [frequency]. Output: forecast series in [format] with confidence range [conf]. Constraints: use simple moving average; avoid overfitting.
  5. Prompt 5 Role: Excel Analysis Specialist. Task: Validate data types and cast all values in [dataset] to correct types on sheet [sheet]. Context: Mixed types in [cols]. Output: a type-consistent table in [format] and a validation log. Constraints: preserve values; provide conversion steps.
  6. Prompt 6 Role: Excel Analysis Specialist. Task: Create a dynamic dashboard header in sheet [sheet] that updates with date, key KPI, and last refresh. Context: Data model on [dataset]. Output: header in [format] with a KPIs summary. Constraints: auto-refresh on dataset change.
  7. Prompt 7 Role: Excel Analysis Specialist. Task: Generate a customer cohort analysis in [dataset], sheet [sheet], grouping by [cohort_col] and measuring [metric_col]. Output: cohort table in [format] plus a cohort retention chart. Constraints: include retention rate calculation and a 3-period comparison.
  8. Prompt 8 Role: Excel Analysis Specialist. Task: Identify and flag potential outliers in [dataset] using IQR method. Context: Data in [sheet], columns [outlier_cols]. Output: a flagged dataset in [format] and an outlier report with rationale. Constraints: describe which data points are considered outliers.
  9. Prompt 9 Role: Excel Analysis Specialist. Task: Normalize currency formats across [dataset], sheet [sheet], converting all values to [currency] with two decimals. Context: Mixed currency inputs in [currency_cols]. Output: normalized values in [format] and a currency map. Constraints: preserve sign and scale.
  10. Prompt 10 Role: Excel Analysis Specialist. Task: Build a KPI scoreboard on sheet [sheet] with columns [kpi_cols], showing trend and status icons. Context: Data from [dataset]; ensure consistent units. Output: a dashboard-ready scoreboard in [format]. Constraints: include color coding and a data refresh button.
  11. Prompt 11 Role: Excel Analysis Specialist. Task: Compare quarterly performance vs prior quarter on [dataset], sheet [sheet], with dimensions [dim_cols] and measures [measure_cols]. Output: variance table in [format] and a chart. Constraints: compute yoy delta where possible.
  12. Prompt 12 Role: Excel Analysis Specialist. Task: Automate data import steps from [source] into [dataset], on sheet [sheet]. Context: Source format is [source_format]; include validation. Output: an import macro outline in [format]. Constraints: reproducible steps, no manual edits.
  13. Prompt 13 Role: Excel Analysis Specialist. Task: Prepare data for Pareto analysis by ranking top contributors in [dataset], sheet [sheet], using columns [id_col], [value_col]. Output: Pareto-ready table in [format] and a summary of top contributions. Constraints: include cumulative share.
  14. Prompt 14 Role: Excel Analysis Specialist. Task: Create a dependency map of formulas used in [sheet], listing each formula, its inputs, and dependencies. Context: Workbook [workbook]. Output: a dependency report in [format]. Constraints: include cross-sheet references.
  15. Prompt 15 Role: Excel Analysis Specialist. Task: Build a Power Pivot data model for [dataset] and create relationships between [table1] and [table2]. Output: data model outline in [format] with hierarchies. Constraints: optimize for performance.
  16. Prompt 16 Role: Excel Analysis Specialist. Task: Apply conditional formatting to highlight anomalies in [dataset], sheet [sheet], with rules for [conditions]. Output: a colored worksheet and a brief explanation of rules. Constraints: use icon sets where helpful.
  17. Prompt 17 Role: Excel Analysis Specialist. Task: Extract unique values and counts from [dataset] on sheet [sheet], columns [cols]. Output: a unique-values table in [format] with counts. Constraints: sort by frequency.
  18. Prompt 18 Role: Excel Analysis Specialist. Task: Run a simple regression on [dataset], sheet [sheet], with dependent variable [dep] and independent variable [ind]. Output: regression results in [format], including R-squared and p-values. Constraints: show residuals.
  19. Prompt 19 Role: Excel Analysis Specialist. Task: Create a scenario planner in [dataset], sheet [sheet], exploring [scenario_col] under [scenario_params]. Output: scenario table in [format] with a narrative summary. Constraints: provide one-page export.
  20. Prompt 20 Role: Excel Analysis Specialist. Task: Validate tax or rate calculations across multiple jurisdictions in [dataset], sheet [sheet], with column [tax_col]. Output: corrected column [corrected_col] and a reconciliation log. Constraints: flag jurisdictional differences.
  21. Prompt 21 Role: Excel Analysis Specialist. Task: Clean and ISO-format dates in [dataset], sheet [sheet], converting sources in [date_cols]. Output: normalized date column [date_norm] and a mapping table. Constraints: preserve time when present.
  22. Prompt 22 Role: Excel Analysis Specialist. Task: Merge datasets from [dataset1] and [dataset2] into sheet [sheet], using key [key_col]. Output: merged table in [format] plus reconciliation notes. Constraints: handle duplicates.
  23. Prompt 23 Role: Excel Analysis Specialist. Task: Find top N customers by revenue in [dataset], sheet [sheet], with threshold [N] and measures [rev_col]. Output: top customers list in [format] and a chart. Constraints: document tie-breakers.
  24. Prompt 24 Role: Excel Analysis Specialist. Task: Compute days sales outstanding (DSO) from invoices in [dataset], sheet [sheet], with columns [invoice_date], [due_date], [amount]. Output: DSO value and a breakdown by [customer]. Constraints: use end-of-month dates.
  25. Prompt 25 Role: Excel Analysis Specialist. Task: Identify seasonality patterns in [dataset], sheet [sheet], using [date_col] and [metric_col]. Output: seasonality report in [format] and a schematic chart. Constraints: specify seasonality type.
  26. Prompt 26 Role: Excel Analysis Specialist. Task: Create a dynamic named range for [range] in [sheet], with automatic expansion. Output: named range setup in [format]. Constraints: ensure workbook-wide accessibility.
  27. Prompt 27 Role: Excel Analysis Specialist. Task: Compute a 95% confidence interval for [metric] in [dataset], sheet [sheet], using [sample_size]. Output: interval values in [format] and interpretation. Constraints: assume normal approximation.
  28. Prompt 28 Role: Excel Analysis Specialist. Task: Build a cost analysis matrix for [project], sheet [sheet], with cost drivers [drivers] and scenarios [scenarios]. Output: matrix in [format] and a summary of key drivers. Constraints: include break-even point.
  29. Prompt 29 Role: Excel Analysis Specialist. Task: Audit formulas for errors across [sheet], listing errors by type [error_types] and location [cell_refs]. Output: error report in [format] with fixes. Constraints: include detection of #N/A and #VALUE!.
  30. Prompt 30 Role: Excel Analysis Specialist. Task: Create a heatmap of key metrics in [dataset], sheet [sheet], with color rules for [conditions]. Output: heatmap ready in [format] and a guide to color scales. Constraints: use diverging palette where appropriate.
  31. Prompt 31 Role: Excel Analysis Specialist. Task: Analyze inventory turns in [dataset], sheet [sheet], using columns [item], [quantity], [cost], [period]. Output: inventory turn table in [format] and a forecast. Constraints: normalize by period length.
  32. Prompt 32 Role: Excel Analysis Specialist. Task: Benchmark salaries across departments in [dataset], sheet [sheet], with dimension [dept] and measure [salary]. Output: benchmarking table in [format] and a percentile distribution. Constraints: adjust for headcount.
  33. Prompt 33 Role: Excel Analysis Specialist. Task: Customer lifetime value (LTV) from transactions in [dataset], sheet [sheet], using columns [customer], [revenue], [period]. Output: LTV table in [format] with assumptions. Constraints: provide sensitivity notes.
  34. Prompt 34 Role: Excel Analysis Specialist. Task: Normalize textual dates in [dataset], sheet [sheet], converting to date values in [date_col]. Output: parsed date column [date_norm] and a validation log. Constraints: report parsing failures.
  35. Prompt 35 Role: Excel Analysis Specialist. Task: Create a dynamic dashboard header in sheet [sheet] showing current date, dataset name [dataset_name], and last refresh. Output: header block in [format]. Constraints: update on data change.
  36. Prompt 36 Role: Excel Analysis Specialist. Task: Compare actuals vs budget in [dataset], sheet [sheet], across [categories] with variance [variance_cols]. Output: variance report in [format] and a chart. Constraints: highlight overages.
  37. Prompt 37 Role: Excel Analysis Specialist. Task: Data validation rules in [sheet], including dropdowns for [list_values] and numeric checks for [nums]. Output: validation setup in [format]. Constraints: prevent invalid inputs.
  38. Prompt 38 Role: Excel Analysis Specialist. Task: Run an Excel regression using Data Analysis Toolpak on [dataset], sheet [sheet], with dependent [dep] and independent [ind]. Output: regression summary in [format] with diagnostics. Constraints: include residual plot notes.
  39. Prompt 39 Role: Excel Analysis Specialist. Task: Build a scenario planner in [dataset], sheet [sheet], exploring [scenario_col] under [scenario_params]. Output: scenario table in [format] with a narrative summary. Constraints: provide one-page export.
  40. Prompt 40 Role: Excel Analysis Specialist. Task: Validate tax calculations across multiple jurisdictions in [dataset], sheet [sheet], with column [tax_col]. Output: corrected column [corrected_col] and a reconciliation log. Constraints: flag jurisdictional differences.
  41. Prompt 41 Role: Excel Analysis Specialist. Task: Clean and ISO-format dates in [dataset], sheet [sheet], converting sources in [date_cols]. Output: normalized date column [date_norm] and a mapping table. Constraints: preserve time when present.
  42. Prompt 42 Role: Excel Analysis Specialist. Task: Merge datasets from [dataset1] and [dataset2] into sheet [sheet], using key [key_col]. Output: merged table in [format] plus reconciliation notes. Constraints: handle duplicates.
  43. Prompt 43 Role: Excel Analysis Specialist. Task: Find top N customers by revenue in [dataset], sheet [sheet], with threshold [N] and measures [rev_col]. Output: top customers list in [format] and a chart. Constraints: document tie-breakers.
  44. Prompt 44 Role: Excel Analysis Specialist. Task: Compute days sales outstanding (DSO) from invoices in [dataset], sheet [sheet], with columns [invoice_date], [due_date], [amount]. Output: DSO value and a breakdown by [customer]. Constraints: use end-of-month dates.
  45. Prompt 45 Role: Excel Analysis Specialist. Task: Identify seasonality patterns in [dataset], sheet [sheet], using [date_col] and [metric_col]. Output: seasonality report in [format] and a schematic chart. Constraints: specify seasonality type.
  46. Prompt 46 Role: Excel Analysis Specialist. Task: Create a dynamic named range for [range] in [sheet], with automatic expansion. Output: named range setup in [format]. Constraints: ensure workbook-wide accessibility.
  47. Prompt 47 Role: Excel Analysis Specialist. Task: Compute a 95% confidence interval for [metric] in [dataset], sheet [sheet], using [sample_size]. Output: interval values in [format] and interpretation. Constraints: assume normal approximation.
  48. Prompt 48 Role: Excel Analysis Specialist. Task: Build a cost analysis matrix for [project], sheet [sheet], with cost drivers [drivers] and scenarios [scenarios]. Output: matrix in [format] and a summary of key drivers. Constraints: include break-even point.
  49. Prompt 49 Role: Excel Analysis Specialist. Task: Audit formulas for errors across [sheet], listing errors by type [error_types] and location [cell_refs]. Output: error report in [format] with fixes. Constraints: include detection of #N/A and #VALUE!.
  50. Prompt 50 Role: Excel Analysis Specialist. Task: Create a heatmap of key metrics in [dataset], sheet [sheet], with color rules for [conditions]. Output: heatmap ready in [format] and a guide to color scales. Constraints: use diverging palette where appropriate.
  51. Prompt 51 Role: Excel Analysis Specialist. Task: Analyze inventory turns in [dataset], sheet [sheet], using columns [item], [quantity], [cost], [period]. Output: inventory turn table in [format] and a forecast. Constraints: normalize by period length.
  52. Prompt 52 Role: Excel Analysis Specialist. Task: Benchmark salaries across departments in [dataset], sheet [sheet], with dimension [dept] and measure [salary]. Output: benchmarking table in [format] and a percentile distribution. Constraints: adjust for headcount.
  53. Prompt 53 Role: Excel Analysis Specialist. Task: Customer lifetime value (LTV) from transactions in [dataset], sheet [sheet], using columns [customer], [revenue], [period]. Output: LTV table in [format] with assumptions. Constraints: provide sensitivity notes.
  54. Prompt 54 Role: Excel Analysis Specialist. Task: Normalize textual dates in [dataset], sheet [sheet], converting to date values in [date_col]. Output: parsed date column [date_norm] and a validation log. Constraints: report parsing failures.
  55. Prompt 55 Role: Excel Analysis Specialist. Task: Create a dynamic dashboard header in sheet [sheet] showing current date, dataset name [dataset_name], and last refresh. Output: header block in [format]. Constraints: update on data change.
  56. Prompt 56 Role: Excel Analysis Specialist. Task: Compare actuals vs budget in [dataset], sheet [sheet], across [categories] with variance [variance_cols]. Output: variance report in [format] and a chart. Constraints: highlight overages.
  57. Prompt 57 Role: Excel Analysis Specialist. Task: Data validation rules in [sheet], including dropdowns for [list_values] and numeric checks for [nums]. Output: validation setup in [format]. Constraints: prevent invalid inputs.
  58. Prompt 58 Role: Excel Analysis Specialist. Task: Run an Excel regression using Data Analysis Toolpak on [dataset], sheet [sheet], with dependent [dep] and independent [ind]. Output: regression summary in [format] with diagnostics. Constraints: include residual plot notes.
  59. Prompt 59 Role: Excel Analysis Specialist. Task: Build a scenario planner in [dataset], sheet [sheet], exploring [scenario_col] under [scenario_params]. Output: scenario table in [format] with a narrative summary. Constraints: provide one-page export.
  60. Prompt 60 Role: Excel Analysis Specialist. Task: Validate tax calculations across multiple jurisdictions in [dataset], sheet [sheet], with column [tax_col]. Output: corrected column [corrected_col] and a reconciliation log. Constraints: flag jurisdictional differences.
  61. Prompt 61 Role: Excel Analysis Specialist. Task: Clean and ISO-format dates in [dataset], sheet [sheet], converting sources in [date_cols]. Output: normalized date column [date_norm] and a mapping table. Constraints: preserve time when present.
  62. Prompt 62 Role: Excel Analysis Specialist. Task: Merge datasets from [dataset1] and [dataset2] into sheet [sheet], using key [key_col]. Output: merged table in [format] plus reconciliation notes. Constraints: handle duplicates.
  63. Prompt 63 Role: Excel Analysis Specialist. Task: Find top N customers by revenue in [dataset], sheet [sheet], with threshold [N] and measures [rev_col]. Output: top customers list in [format] and a chart. Constraints: document tie-breakers.
  64. Prompt 64 Role: Excel Analysis Specialist. Task: Compute days sales outstanding (DSO) from invoices in [dataset], sheet [sheet], with columns [invoice_date], [due_date], [amount]. Output: DSO value and a breakdown by [customer]. Constraints: use end-of-month dates.
  65. Prompt 65 Role: Excel Analysis Specialist. Task: Identify seasonality patterns in [dataset], sheet [sheet], using [date_col] and [metric_col]. Output: seasonality report in [format] and a schematic chart. Constraints: specify seasonality type.
  66. Prompt 66 Role: Excel Analysis Specialist. Task: Create a dynamic named range for [range] in [sheet], with automatic expansion. Output: named range setup in [format]. Constraints: ensure workbook-wide accessibility.
  67. Prompt 67 Role: Excel Analysis Specialist. Task: Compute a 95% confidence interval for [metric] in [dataset], sheet [sheet], using [sample_size]. Output: interval values in [format] and interpretation. Constraints: assume normal approximation.
  68. Prompt 68 Role: Excel Analysis Specialist. Task: Build a cost analysis matrix for [project], sheet [sheet], with cost drivers [drivers] and scenarios [scenarios]. Output: matrix in [format] and a summary of key drivers. Constraints: include break-even point.
  69. Prompt 69 Role: Excel Analysis Specialist. Task: Audit formulas for errors across [sheet], listing errors by type [error_types] and location [cell_refs]. Output: error report in [format] with fixes. Constraints: include detection of #N/A and #VALUE!.
  70. Prompt 70 Role: Excel Analysis Specialist. Task: Create a heatmap of key metrics in [dataset], sheet [sheet], with color rules for [conditions]. Output: heatmap ready in [format] and a guide to color scales. Constraints: use diverging palette where appropriate.
  71. Prompt 71 Role: Excel Analysis Specialist. Task: Analyze inventory turns in [dataset], sheet [sheet], using columns [item], [quantity], [cost], [period]. Output: inventory turn table in [format] and a forecast. Constraints: normalize by period length.
  72. Prompt 72 Role: Excel Analysis Specialist. Task: Benchmark salaries across departments in [dataset], sheet [sheet], with dimension [dept] and measure [salary]. Output: benchmarking table in [format] and a percentile distribution. Constraints: adjust for headcount.
  73. Prompt 73 Role: Excel Analysis Specialist. Task: Customer lifetime value (LTV) from transactions in [dataset], sheet [sheet], using columns [customer], [revenue], [period]. Output: LTV table in [format] with assumptions. Constraints: provide sensitivity notes.
  74. Prompt 74 Role: Excel Analysis Specialist. Task: Normalize textual dates in [dataset], sheet [sheet], converting to date values in [date_col]. Output: parsed date column [date_norm] and a validation log. Constraints: report parsing failures.
  75. Prompt 75 Role: Excel Analysis Specialist. Task: Create a dynamic dashboard header in sheet [sheet] showing current date, dataset name [dataset_name], and last refresh. Output: header block in [format]. Constraints: update on data change.
  76. Prompt 76 Role: Excel Analysis Specialist. Task: Compare actuals vs budget in [dataset], sheet [sheet], across [categories] with variance [variance_cols]. Output: variance report in [format] and a chart. Constraints: highlight overages.
  77. Prompt 77 Role: Excel Analysis Specialist. Task: Data validation rules in [sheet], including dropdowns for [list_values] and numeric checks for [nums]. Output: validation setup in [format]. Constraints: prevent invalid inputs.
  78. Prompt 78 Role: Excel Analysis Specialist. Task: Run an Excel regression using Data Analysis Toolpak on [dataset], sheet [sheet], with dependent [dep] and independent [ind]. Output: regression summary in [format] with diagnostics. Constraints: include residual plot notes.
  79. Prompt 79 Role: Excel Analysis Specialist. Task: Build a scenario planner in [dataset], sheet [sheet], exploring [scenario_col] under [scenario_params]. Output: scenario table in [format] with a narrative summary. Constraints: provide one-page export.
  80. Prompt 80 Role: Excel Analysis Specialist. Task: Validate tax calculations across multiple jurisdictions in [dataset], sheet [sheet], with column [tax_col]. Output: corrected column [corrected_col] and a reconciliation log. Constraints: flag jurisdictional differences.
  81. Prompt 81 Role: Excel Analysis Specialist. Task: Clean and ISO-format dates in [dataset], sheet [sheet], converting sources in [date_cols]. Output: normalized date column [date_norm] and a mapping table. Constraints: preserve time when present.
  82. Prompt 82 Role: Excel Analysis Specialist. Task: Merge datasets from [dataset1] and [dataset2] into sheet [sheet], using key [key_col]. Output: merged table in [format] plus reconciliation notes. Constraints: handle duplicates.
  83. Prompt 83 Role: Excel Analysis Specialist. Task: Find top N customers by revenue in [dataset], sheet [sheet], with threshold [N] and measures [rev_col]. Output: top customers list in [format] and a chart. Constraints: document tie-breakers.
  84. Prompt 84 Role: Excel Analysis Specialist. Task: Compute days sales outstanding (DSO) from invoices in [dataset], sheet [sheet], with columns [invoice_date], [due_date], [amount]. Output: DSO value and a breakdown by [customer]. Constraints: use end-of-month dates.
  85. Prompt 85 Role: Excel Analysis Specialist. Task: Identify seasonality patterns in [dataset], sheet [sheet], using [date_col] and [metric_col]. Output: seasonality report in [format] and a schematic chart. Constraints: specify seasonality type.
  86. Prompt 86 Role: Excel Analysis Specialist. Task: Create a dynamic named range for [range] in [sheet], with automatic expansion. Output: named range setup in [format]. Constraints: ensure workbook-wide accessibility.
  87. Prompt 87 Role: Excel Analysis Specialist. Task: Compute a 95% confidence interval for [metric] in [dataset], sheet [sheet], using [sample_size]. Output: interval values in [format] and interpretation. Constraints: assume normal approximation.
  88. Prompt 88 Role: Excel Analysis Specialist. Task: Build a cost analysis matrix for [project], sheet [sheet], with cost drivers [drivers] and scenarios [scenarios]. Output: matrix in [format] and a summary of key drivers. Constraints: include break-even point.
  89. Prompt 89 Role: Excel Analysis Specialist. Task: Audit formulas for errors across [sheet], listing errors by type [error_types] and location [cell_refs]. Output: error report in [format] with fixes. Constraints: include detection of #N/A and #VALUE!.
  90. Prompt 90 Role: Excel Analysis Specialist. Task: Create a heatmap of key metrics in [dataset], sheet [sheet], with color rules for [conditions]. Output: heatmap ready in [format] and a guide to color scales. Constraints: use diverging palette where appropriate.
  91. Prompt 91 Role: Excel Analysis Specialist. Task: Analyze inventory turns in [dataset], sheet [sheet], using columns [item], [quantity], [cost], [period]. Output: inventory turn table in [format] and a forecast. Constraints: normalize by period length.
  92. Prompt 92 Role: Excel Analysis Specialist. Task: Benchmark salaries across departments in [dataset], sheet [sheet], with dimension [dept] and measure [salary]. Output: benchmarking table in [format] and a percentile distribution. Constraints: adjust for headcount.
  93. Prompt 93 Role: Excel Analysis Specialist. Task: Customer lifetime value (LTV) from transactions in [dataset], sheet [sheet], using columns [customer], [revenue], [period]. Output: LTV table in [format] with assumptions. Constraints: provide sensitivity notes.
  94. Prompt 94 Role: Excel Analysis Specialist. Task: Normalize textual dates in [dataset], sheet [sheet], converting to date values in [date_col]. Output: parsed date column [date_norm] and a validation log. Constraints: report parsing failures.
  95. Prompt 95 Role: Excel Analysis Specialist. Task: Create a dynamic dashboard header in sheet [sheet] showing current date, dataset name [dataset_name], and last refresh. Output: header block in [format]. Constraints: update on data change.
  96. Prompt 96 Role: Excel Analysis Specialist. Task: Compare actuals vs budget in [dataset], sheet [sheet], across [categories] with variance [variance_cols]. Output: variance report in [format] and a chart. Constraints: highlight overages.
  97. Prompt 97 Role: Excel Analysis Specialist. Task: Data validation rules in [sheet], including dropdowns for [list_values] and numeric checks for [nums]. Output: validation setup in [format]. Constraints: prevent invalid inputs.
  98. Prompt 98 Role: Excel Analysis Specialist. Task: Run an Excel regression using Data Analysis Toolpak on [dataset], sheet [sheet], with dependent [dep] and independent [ind]. Output: regression summary in [format] with diagnostics. Constraints: include residual plot notes.
  99. Prompt 99 Role: Excel Analysis Specialist. Task: Build a scenario planner in [dataset], sheet [sheet], exploring [scenario_col] under [scenario_params]. Output: scenario table in [format] with a narrative summary. Constraints: provide one-page export.
  100. Prompt 100 Role: Excel Analysis Specialist. Task: Build an executive metrics dashboard in [dataset], sheet [sheet], pulling key metrics from [sources]. Output: dashboard outline in [format] with recommended layout. Constraints: scoping and export format specified.

Markdown Template

100 Best Gemini Prompts for Excel Analysis

# 100 Best Gemini Prompts for Excel Analysis

**Prompt 1**: Role: Excel Analysis Specialist. Task: Clean and standardize the dataset in [dataset], on sheet [sheet], focusing on date and numeric fields. Context: Input contains inconsistent date formats in [date_column], empty values in [cols_missing], and numeric fields stored as text in [num_cols]. Output: a cleaned dataset in [format] with a new summary column [summary_col] and a log of changes. Constraints: preserve original rows, document all changes, reproducible steps, and use only Excel functions.
**Prompt 2**: Role: Excel Analysis Specialist. Task: Identify and remove duplicates while preserving the most complete record in [dataset], sheet [sheet]. Context: Duplicates found across columns [dup_cols]; maintain unique keys in [key_cols]. Output: a deduplicated dataset in [format] with a report of duplicates removed. Constraints: keep first occurrence, log original row numbers.
**Prompt 3**: Role: Excel Analysis Specialist. Task: Pivot analysis to summarize sales by region and month. Context: Data in [dataset], sheet [sheet], columns [region_col], [month_col], [sales_col]. Output: a pivot table in [format] with totals and a 3-month moving average column. Constraints: show grand totals and include a filter for [region_filter].
**Prompt 4**: Role: Excel Analysis Specialist. Task: Build a moving average forecast for [metric] over [periods] in [dataset], sheet [sheet]. Context: Time series in [date_col] with frequency [frequency]. Output: forecast series in [format] with confidence range [conf]. Constraints: use simple moving average; avoid overfitting.
**Prompt 5**: Role: Excel Analysis Specialist. Task: Validate data types and cast all values in [dataset] to correct types on sheet [sheet]. Context: Mixed types in [cols]. Output: a type-consistent table in [format] and a validation log. Constraints: preserve values; provide conversion steps.
**Prompt 6**: Role: Excel Analysis Specialist. Task: Create a dynamic dashboard header in sheet [sheet] that updates with date, key KPI, and last refresh. Context: Data model on [dataset]. Output: header in [format] with a KPIs summary. Constraints: auto-refresh on dataset change.
**Prompt 7**: Role: Excel Analysis Specialist. Task: Generate a customer cohort analysis in [dataset], sheet [sheet], grouping by [cohort_col] and measuring [metric_col]. Output: cohort table in [format] plus a cohort retention chart. Constraints: include retention rate calculation and a 3-period comparison.
**Prompt 8**: Role: Excel Analysis Specialist. Task: Identify and flag potential outliers in [dataset] using IQR method. Context: Data on [sheet], columns [outlier_cols]. Output: a flagged dataset in [format] and an outlier report with rationale. Constraints: describe which data points are considered outliers.
**Prompt 9**: Role: Excel Analysis Specialist. Task: Normalize currency formats across [dataset], sheet [sheet], converting all values to [currency] with two decimals. Context: Mixed currency inputs in [currency_cols]. Output: normalized values in [format] and a currency map. Constraints: preserve sign and scale.
**Prompt 10**: Role: Excel Analysis Specialist. Task: Build a KPI scoreboard on sheet [sheet] with columns [kpi_cols], showing trend and status icons. Context: Data from [dataset]; ensure consistent units. Output: a dashboard-ready scoreboard in [format]. Constraints: include color coding and a data refresh button.
**Prompt 11**: Role: Excel Analysis Specialist. Task: Compare quarterly performance vs prior quarter on [dataset], sheet [sheet], with dimensions [dim_cols] and measures [measure_cols]. Output: variance table in [format] and a chart. Constraints: compute yoy delta where possible.
**Prompt 12**: Role: Excel Analysis Specialist. Task: Automate data import steps from [source] into [dataset], on sheet [sheet]. Context: Source format is [source_format]; include validation. Output: an import macro outline in [format]. Constraints: reproducible steps, no manual edits.
**Prompt 13**: Role: Excel Analysis Specialist. Task: Prepare data for Pareto analysis by ranking top contributors in [dataset], sheet [sheet], using columns [id_col], [value_col]. Output: Pareto-ready table in [format] and a summary of top contributions. Constraints: include cumulative share.
**Prompt 14**: Role: Excel Analysis Specialist. Task: Create a dependency map of formulas used in [sheet], listing each formula, its inputs, and dependencies. Context: Workbook [workbook]. Output: a dependency report in [format]. Constraints: include cross-sheet references.
**Prompt 15**: Role: Excel Analysis Specialist. Task: Build a Power Pivot data model for [dataset] and create relationships between [table1] and [table2]. Output: data model outline in [format] with hierarchies. Constraints: optimize for performance.
**Prompt 16**: Role: Excel Analysis Specialist. Task: Apply conditional formatting to highlight anomalies in [dataset], sheet [sheet], with rules for [conditions]. Output: a colored worksheet and a brief explanation of rules. Constraints: use icon sets where helpful.
**Prompt 17**: Role: Excel Analysis Specialist. Task: Extract unique values and counts from [dataset] on sheet [sheet], columns [cols]. Output: a unique-values table in [format] with counts. Constraints: sort by frequency.
**Prompt 18**: Role: Excel Analysis Specialist. Task: Run a simple regression on [dataset], sheet [sheet], with dependent variable [dep] and independent variable [ind]. Output: regression results in [format], including R-squared and p-values. Constraints: show residuals.
**Prompt 19**: Role: Excel Analysis Specialist. Task: Create a scenario planner in [dataset], sheet [sheet], exploring [scenario_col] under [scenario_params]. Output: scenario table in [format] with a narrative summary. Constraints: provide one-page export.
**Prompt 20**: Role: Excel Analysis Specialist. Task: Validate tax or rate calculations across [dataset], sheet [sheet], ensuring correct tax_rate in [tax_col]. Output: corrected column [corrected_col] and a validation log. Constraints: flag mismatches.
**Prompt 21**: Role: Excel Analysis Specialist. Task: Clean and ISO-format dates in [dataset], sheet [sheet], with source dates in [date_cols]. Output: normalized date column [date_norm] and a mapping table. Constraints: preserve original date values.
**Prompt 22**: Role: Excel Analysis Specialist. Task: Merge datasets from [dataset1] and [dataset2] into sheet [sheet], using key [key_col]. Output: merged table in [format] with a reconciliation log. Constraints: handle non-matching keys gracefully.
**Prompt 23**: Role: Excel Analysis Specialist. Task: Find top N customers by revenue in [dataset], sheet [sheet], with threshold [N] and measures [rev_col]. Output: top customers list in [format] and a chart. Constraints: document tie-breakers.
**Prompt 24**: Role: Excel Analysis Specialist. Task: Compute days sales outstanding (DSO) from invoices in [dataset], sheet [sheet], with columns [invoice_date], [due_date], [amount]. Output: DSO value and a breakdown by [customer]. Constraints: use end-of-month dates.
**Prompt 25**: Role: Excel Analysis Specialist. Task: Identify seasonality patterns in [dataset], sheet [sheet], using [date_col] and [metric_col]. Output: seasonality report in [format] and a schematic chart. Constraints: specify seasonality type.
**Prompt 26**: Role: Excel Analysis Specialist. Task: Create a dynamic named range for [range] in [sheet], with automatic expansion. Output: named range setup in [format]. Constraints: ensure workbook-wide accessibility.
**Prompt 27**: Role: Excel Analysis Specialist. Task: Compute a 95% confidence interval for [metric] in [dataset], sheet [sheet], using [sample_size]. Output: interval values in [format] and interpretation. Constraints: assume normal approximation.
**Prompt 28**: Role: Excel Analysis Specialist. Task: Build a cost analysis matrix for [project], sheet [sheet], with cost drivers [drivers] and scenarios [scenarios]. Output: matrix in [format] and a summary of key drivers. Constraints: include break-even point.
**Prompt 29**: Role: Excel Analysis Specialist. Task: Audit formulas for errors across [sheet], listing errors by type [error_types] and location [cell_refs]. Output: error report in [format] with fixes. Constraints: include detection of #N/A and #VALUE!.
**Prompt 30**: Role: Excel Analysis Specialist. Task: Create a heatmap of key metrics in [dataset], sheet [sheet], with color rules for [conditions]. Output: heatmap ready in [format] and a guide to color scales. Constraints: use diverging palette where appropriate.
**Prompt 31**: Role: Excel Analysis Specialist. Task: Analyze inventory turns in [dataset], sheet [sheet], using columns [item], [quantity], [cost], [period]. Output: inventory turn table in [format] and a forecast. Constraints: normalize by period length.
**Prompt 32**: Role: Excel Analysis Specialist. Task: Benchmark salaries across departments in [dataset], sheet [sheet], with dimension [dept] and measure [salary]. Output: benchmarking table in [format] and a percentile distribution. Constraints: adjust for headcount.
**Prompt 33**: Role: Excel Analysis Specialist. Task: Compute customer lifetime value (LTV) from transactions in [dataset], sheet [sheet], using columns [customer], [revenue], [period]. Output: LTV table in [format] with assumptions. Constraints: provide sensitivity notes.
**Prompt 34**: Role: Excel Analysis Specialist. Task: Normalize textual dates in [dataset], sheet [sheet], converting to date values in [date_col]. Output: parsed date column [date_norm] and a validation log. Constraints: report parsing failures.
**Prompt 35**: Role: Excel Analysis Specialist. Task: Create a dynamic dashboard header in sheet [sheet] showing current date, dataset name [dataset_name], and last refresh. Output: header block in [format]. Constraints: update on data change.
**Prompt 36**: Role: Excel Analysis Specialist. Task: Compare actuals vs budget in [dataset], sheet [sheet], across [categories] with variance [variance_cols]. Output: variance report in [format] and a chart. Constraints: highlight overages.
**Prompt 37**: Role: Excel Analysis Specialist. Task: Implement data validation rules in [sheet], including dropdowns for [list_values] and numeric checks for [nums]. Output: validation setup in [format]. Constraints: prevent invalid inputs.
**Prompt 38**: Role: Excel Analysis Specialist. Task: Run an Excel regression using Data Analysis Toolpak on [dataset], sheet [sheet], with dependent [dep] and independent [ind]. Output: regression summary in [format] with diagnostics. Constraints: include residual plot notes.
**Prompt 39**: Role: Excel Analysis Specialist. Task: Build a scenario planner in [dataset], sheet [sheet], exploring [scenario_columns] under [scenario_values]. Output: scenario table in [format] with a narrative. Constraints: provide best-case and worst-case ranges.
**Prompt 40**: Role: Excel Analysis Specialist. Task: Validate tax calculations across multiple jurisdictions in [dataset], sheet [sheet], with column [tax_col]. Output: corrected [corrected_col] and a reconciliation log. Constraints: flag jurisdictional differences.
**Prompt 41**: Role: Excel Analysis Specialist. Task: Clean and standardize dates to ISO in [dataset], sheet [sheet], converting sources in [date_cols]. Output: ISO_date column [iso_date] and a mapping table. Constraints: preserve time when present.
**Prompt 42**: Role: Excel Analysis Specialist. Task: Merge two data sources in [dataset1] and [dataset2] on [key_col] into sheet [sheet]. Output: merged dataset in [format] plus reconciliation notes. Constraints: handle duplicates.
**Prompt 43**: Role: Excel Analysis Specialist. Task: Identify the top N products by gross margin in [dataset], sheet [sheet], with margin column [margin] and top N [N]. Output: top products table in [format] and a bar chart. Constraints: include margin percent.
**Prompt 44**: Role: Excel Analysis Specialist. Task: Create a date breakdown (year, quarter, month) from [date_col] in [dataset], sheet [sheet]. Output: breakdown table in [format] and a pivot-ready structure. Constraints: ensure exact quarter labels.
**Prompt 45**: Role: Excel Analysis Specialist. Task: Build a tall-to-wide transformation in [dataset], sheet [sheet], pivoting on [pivot_cols] to produce a cross-tab matrix. Output: transformed dataset in [format]. Constraints: preserve data integrity.
**Prompt 46**: Role: Excel Analysis Specialist. Task: Compute Z-scores for measures in [dataset], sheet [sheet], with columns [zs_cols]. Output: z-score table in [format] and a summary interpretation. Constraints: handle missing values gracefully.
**Prompt 47**: Role: Excel Analysis Specialist. Task: Run a Monte Carlo-like forecast in Excel for [metric] using [distribution] on dataset [dataset], sheet [sheet]. Output: forecast range in [format] and probability notes. Constraints: specify random seed.
**Prompt 48**: Role: Excel Analysis Specialist. Task: Create a simple linear regression model in Excel for [dataset], sheet [sheet], with dependent [dep], independent [ind]. Output: model equation and R-squared in [format]. Constraints: assume linear relationship.
**Prompt 49**: Role: Excel Analysis Specialist. Task: Build a dashboard with slicers for [dimension], [measure] on sheet [sheet], using data model from [dataset]. Output: interactive workbook outline in [format]. Constraints: ensure slicer synchronization.
**Prompt 50**: Role: Excel Analysis Specialist. Task: Clean product names in [dataset], sheet [sheet], removing duplicates and standardizing suffixes in [suffix_list]. Output: cleaned name column [clean_name] and a mapping table. Constraints: preserve original name when ambiguous.
**Prompt 51**: Role: Excel Analysis Specialist. Task: Detect seasonality with moving averages in [dataset], sheet [sheet], using [window] periods. Output: seasonality report in [format] and a chart. Constraints: annotate seasonality strength.
**Prompt 52**: Role: Excel Analysis Specialist. Task: Budget vs actual variance by department in [dataset], sheet [sheet], with [dept_cols] and [amount_cols]. Output: variance table in [format] and a highlight column for variances. Constraints: include percentage variance.
**Prompt 53**: Role: Excel Analysis Specialist. Task: Calculate revenue per unit in [dataset], sheet [sheet], using [revenue_col] and [units_col]. Output: per-unit table in [format] with unit-level insights. Constraints: handle zero units.
**Prompt 54**: Role: Excel Analysis Specialist. Task: Customer segmentation by RFM metrics in [dataset], sheet [sheet], with recency [R], frequency [F], monetary [M]. Output: segments in [format] and recommended actions. Constraints: ensure segment sizes are meaningful.
**Prompt 55**: Role: Excel Analysis Specialist. Task: Analyze employee performance over time in [dataset], sheet [sheet], with KPIs [kpis]. Output: performance trend table in [format] and an interpretation note. Constraints: compare against targets.
**Prompt 56**: Role: Excel Analysis Specialist. Task: Supplier lead time analysis in [dataset], sheet [sheet], with [supplier], [order_date], [delivery_date]. Output: lead time distribution in [format] and outliers. Constraints: compute average and median.
**Prompt 57**: Role: Excel Analysis Specialist. Task: Compute inventory safety stock using [demand], [lead_time], [service_level] in [dataset], sheet [sheet]. Output: safety stock values in [format]. Constraints: provide recomputed reorder point.
**Prompt 58**: Role: Excel Analysis Specialist. Task: Estimate price elasticity in [dataset], sheet [sheet], with columns [price], [quantity]. Output: elasticity estimate in [format] and scenario notes. Constraints: document assumptions.
**Prompt 59**: Role: Excel Analysis Specialist. Task: Compute Z-score-based flags for quality control in [dataset], sheet [sheet], with [cols]. Output: flagged rows in [format] and a QC summary. Constraints: set thresholds clearly.
**Prompt 60**: Role: Excel Analysis Specialist. Task: Create a trendline forecast for [metric] on a time axis [date_col] in [dataset], sheet [sheet]. Output: forecast and equation in [format]. Constraints: include confidence band notes.
**Prompt 61**: Role: Excel Analysis Specialist. Task: Aggregate weekly metrics into monthly totals in [dataset], sheet [sheet], with [week_col] and [monthly_col]. Output: monthly summary in [format] and a chart. Constraints: handle partial weeks.
**Prompt 62**: Role: Excel Analysis Specialist. Task: Build a cross-tab matrix from [dataset], sheet [sheet] with [row_dims] and [col_dims]. Output: cross-tab in [format]. Constraints: include grand totals.
**Prompt 63**: Role: Excel Analysis Specialist. Task: Data quality scorecard for [dataset], sheet [sheet], with dimensions [dq_dims]. Output: scorecard in [format] and recommended cleanup actions. Constraints: assign weights.
**Prompt 64**: Role: Excel Analysis Specialist. Task: Identify data gaps in [dataset], sheet [sheet], and propose enrichment sources in [sources]. Output: gap report in [format] and enrichment plan. Constraints: quantify impact.
**Prompt 65**: Role: Excel Analysis Specialist. Task: Map tax code to categories in [dataset], sheet [sheet], with [code_col]. Output: mapped tax categories in [format] and a reconciliation log. Constraints: handle unmapped codes.
**Prompt 66**: Role: Excel Analysis Specialist. Task: Currency conversion and rounding in [dataset], sheet [sheet], from [base_currency] to [target_currency]. Output: converted amounts in [format] with two decimals. Constraints: ensure precision consistency.
**Prompt 67**: Role: Excel Analysis Specialist. Task: Create an age bucket analysis from [date_col] in [dataset], sheet [sheet], with buckets [buckets]. Output: age distribution in [format] and a bar chart. Constraints: use integer ages.
**Prompt 68**: Role: Excel Analysis Specialist. Task: KPI heatmap in [dataset], sheet [sheet], across [kpi_list]. Output: heatmap in [format] with color scale. Constraints: ensure readability.
**Prompt 69**: Role: Excel Analysis Specialist. Task: Map geolocation counts in Excel from [address_col] using approximate regions in [dataset], sheet [sheet]. Output: regional counts in [format] and a map-ready table. Constraints: respect privacy.
**Prompt 70**: Role: Excel Analysis Specialist. Task: Forecast demand by season for [product] in [dataset], sheet [sheet], using [season_cols]. Output: forecast table in [format] and a narrative. Constraints: address seasonality.
**Prompt 71**: Role: Excel Analysis Specialist. Task: Create a waterfall chart data structure in [dataset], sheet [sheet], with [inflows], [outflows]. Output: waterfall inputs in [format]. Constraints: include totals.
**Prompt 72**: Role: Excel Analysis Specialist. Task: Identify cross-sell opportunities from transaction data in [dataset], sheet [sheet], using [product_cols], [customer_cols]. Output: recommendations in [format] and a chart. Constraints: back up with data lines.
**Prompt 73**: Role: Excel Analysis Specialist. Task: Split text fields into columns on [dataset], sheet [sheet], using delimiter [delim]. Output: split columns in [format] and a sample check. Constraints: handle edge cases.
**Prompt 74**: Role: Excel Analysis Specialist. Task: Extract year, month, day from date column [date_col] in [dataset], sheet [sheet]. Output: [year], [month], [day] columns in [format]. Constraints: preserve original date.
**Prompt 75**: Role: Excel Analysis Specialist. Task: Build a risk assessment matrix in [dataset], sheet [sheet], with risk axes [axes]. Output: matrix in [format] and a mitigation plan. Constraints: include likelihood and impact scoring.
**Prompt 76**: Role: Excel Analysis Specialist. Task: Data anonymization and hashing in [dataset], sheet [sheet], with sensitive columns [sens_cols]. Output: anonymized table in [format]. Constraints: preserve structure for analysis.
**Prompt 77**: Role: Excel Analysis Specialist. Task: Analyze EBITDA margin in [dataset], sheet [sheet], using [rev_col] and [cost_col]. Output: margin analysis in [format] and a trend chart. Constraints: exclude non-operating items.
**Prompt 78**: Role: Excel Analysis Specialist. Task: Perform break-even analysis for [product] in [dataset], sheet [sheet], with fixed cost [fixed], variable cost [var]. Output: break-even point in [format] and a sensitivity table. Constraints: show units.
**Prompt 79**: Role: Excel Analysis Specialist. Task: Analyze customer churn in [dataset], sheet [sheet], with [customer_cols]. Output: churn rate by segment in [format] and action notes. Constraints: compute cohort churn.
**Prompt 80**: Role: Excel Analysis Specialist. Task: Create a data-driven decision log from [dataset], sheet [sheet], capturing decisions, notes, and owners in [log_cols]. Output: decision log in [format]. Constraints: ensure traceability.
**Prompt 81**: Role: Excel Analysis Specialist. Task: Ageing analysis of receivables in [dataset], sheet [sheet], with [due_date] and [balance]. Output: ageing buckets in [format] and aging trend. Constraints: categorize over 30/60/90 days.
**Prompt 82**: Role: Excel Analysis Specialist. Task: Use SUMPRODUCT to compute weighted averages in [dataset], sheet [sheet], with [weights] and [values]. Output: weighted averages table in [format]. Constraints: avoid array overload.
**Prompt 83**: Role: Excel Analysis Specialist. Task: Create a dynamic KPI card in sheet [sheet] displaying [kpi_list] with live updates from [dataset]. Output: KPI card in [format]. Constraints: ensure readability.
**Prompt 84**: Role: Excel Analysis Specialist. Task: Build a stacked bar chart data structure in [dataset], sheet [sheet], grouping by [stack_cols]. Output: chart-ready data in [format]. Constraints: include legend and data labels.
**Prompt 85**: Role: Excel Analysis Specialist. Task: Implement moving average crossover signals in [dataset], sheet [sheet], with [short_window], [long_window]. Output: signals table in [format] and a chart. Constraints: avoid false signals.
**Prompt 86**: Role: Excel Analysis Specialist. Task: Compare store performance by location in [dataset], sheet [sheet], across [location_cols] and [metrics]. Output: comparative report in [format] and a map-ready breakdown. Constraints: normalize by store size.
**Prompt 87**: Role: Excel Analysis Specialist. Task: Identify product category dominance in [dataset], sheet [sheet], with [category_col] and [sales_col]. Output: dominance table in [format] and a chart. Constraints: rank with ties.
**Prompt 88**: Role: Excel Analysis Specialist. Task: Compute weighted lead time for orders in [dataset], sheet [sheet], using [lead_time], [order_value] as weight. Output: weighted lead time table in [format]. Constraints: default to median when data sparse.
**Prompt 89**: Role: Excel Analysis Specialist. Task: Data validation with dropdowns for [list_values] in sheet [sheet], ensuring inputs are valid for [field]. Output: validation setup in [format]. Constraints: provide error messages.
**Prompt 90**: Role: Excel Analysis Specialist. Task: Create a predictive score using simple formulas in [dataset], sheet [sheet], with inputs [features]. Output: score column [score_col] and a short interpretation in [format]. Constraints: keep model transparent.
**Prompt 91**: Role: Excel Analysis Specialist. Task: Analyze time-to-resolution metrics in [dataset], sheet [sheet], using [start_col] and [end_col]. Output: time-to-resolution table in [format] and a chart. Constraints: handle missing dates.
**Prompt 92**: Role: Excel Analysis Specialist. Task: Interrogate data quality with formulas in [dataset], sheet [sheet], listing quality issues by type [issues]. Output: quality report in [format]. Constraints: categorize severity.
**Prompt 93**: Role: Excel Analysis Specialist. Task: Reconcile data between [sheet1] and [sheet2] in [workbook], using key [key_col]. Output: reconciliation table in [format]. Constraints: flag mismatches with reasons.
**Prompt 94**: Role: Excel Analysis Specialist. Task: Revenue by channel analysis in [dataset], sheet [sheet], with [channel_col] and [revenue_col]. Output: channel breakdown in [format] and a chart. Constraints: normalize for seasonality.
**Prompt 95**: Role: Excel Analysis Specialist. Task: Identify duplicates across multiple columns in [dataset], sheet [sheet], including [cols]. Output: duplicates list in [format] and a deduplicate plan. Constraints: account for near-duplicates.
**Prompt 96**: Role: Excel Analysis Specialist. Task: Create a rolling sum for [metric] over [window] periods in [dataset], sheet [sheet]. Output: rolling sum series in [format]. Constraints: handle missing periods.
**Prompt 97**: Role: Excel Analysis Specialist. Task: Build a Gantt-like chart data in Excel for [project], sheet [sheet], with [tasks], [start], [end]. Output: chart-ready data in [format]. Constraints: ensure non-overlapping tasks clarity.
**Prompt 98**: Role: Excel Analysis Specialist. Task: Calculate cash conversion cycle for [company] in [dataset], sheet [sheet], with [inventory], [receivables], [payables]. Output: CCC table in [format]. Constraints: provide interpretation.
**Prompt 99**: Role: Excel Analysis Specialist. Task: Analyze price change impact on demand in [dataset], sheet [sheet], with [price_col] and [quantity_col]. Output: impact table in [format] and a sensitivity note. Constraints: avoid overfitting price elasticity.
**Prompt 100**: Role: Excel Analysis Specialist. Task: Build an executive metrics dashboard in [dataset], sheet [sheet], pulling key metrics from [sources]. Output: dashboard outline in [format] with recommended layout. Constraints: scoping and export format specified.

Best Practices

Use consistent placeholder conventions, test prompts in a sample workbook, and document outputs for auditability. Ensure prompts are self-contained with clear roles, tasks, and constraints. Prefer explicit output formats (table, chart, log) and avoid overfitting prompts to a single dataset.

Common Mistakes to Avoid

Avoid vague tasks, missing placeholders, unclear output formats, and prompts that assume tools beyond Excel. Do not overstate capabilities or imply non-deterministic results without instructions for reproducibility.

Related resources

Use these related resources to connect this Gemini prompt library with practical AI workflows, implementation examples, blog analysis, and business use cases.

FAQ

What is the goal of this Gemini prompts library for Excel Analysis?

To provide a complete, copyable set of prompts that guide Gemini to perform reliable Excel data cleaning, analysis, forecasting, and dashboard tasks without extra context.

How should I customize prompts for my workbook?

Replace placeholders like [dataset], [sheet], [cols], and [scenario] with your actual workbook names and column headers before running the prompt, and specify output formats clearly.

Can I mix prompts to build a full Excel workflow?

Yes. Use prompts to prepare data, build analyses, and generate dashboards, then chain outputs into your workbook sections with consistent naming and placement.

Are these prompts safe to run on sensitive data?

Prompts can be adapted for sensitive data by adding data masking steps and restricting outputs to summary tables and non-identifying fields. Always follow data governance policies.