100 Best Claude Prompts for Excel Analysis

Introduction

This page is a practical prompt library for Claude prompts focused on Excel Analysis. It is intended for data analysts, business intelligence engineers, and anyone who uses Excel as a primary data analysis tool and wants to leverage Claude AI prompts to speed up insights.

In this prompt library we provide a ready to copy set of prompts designed to handle data cleaning, trend analysis, forecasting, and dashboard storytelling within Excel workflows.

Direct Answer

The best Claude prompts for Excel Analysis are a curated set of prompts designed to maximize Excel data analysis interactions with Claude AI. They cover data cleaning, trend detection, forecasting, and dashboard storytelling to help you extract actionable insights from Excel datasets.

How to Use These Claude Prompts

• Replace placeholders like [dataset_description], [date], [column], [metric] with your actual data context before sending to Claude.
• Combine prompts with explicit output formats such as CSV JSON or a structured table when needed.
• Add constraints to enforce clarity and safety, for example avoid extrapolations beyond observed data unless explicitly requested.
• Request structured outputs that are easy to verify in Excel or downstream tools. Always validate results against known summaries.
• Test prompts on small samples to ensure reliability and adjust placeholders to fit your dataset structure.

100 Best Claude Prompts for Excel Analysis

1. Data trend summary Role: You are an Excel data analyst assistant. Task: Generate a data driven trend summary for the dataset described by [dataset_description]. Context: The sheet includes columns [date], [value], [category], with data spanning [date_range]. Output: A concise trend summary in bullet points plus a one period forecast for [period]. Constraints: Provide results in a parseable format if requested, avoid speculation beyond the data, note data gaps and data quality notes.
2. Top 10 customers by revenue Role: You are an Excel data analyst assistant. Task: Identify the top 10 customers by revenue from the dataset described by [dataset_description]. Context: Columns include [customer_id], [customer_name], [revenue], [date]. Output: List top 10 customers with revenue amounts and a one sentence note on seasonality or pattern. Constraints: Use integer currency values, present in descending order, include total of top 10 and bottom 5 as reference.
3. Quarterly revenue growth analysis Role: You are an Excel data analyst assistant. Task: Compute quarterly revenue growth and identify the strongest and weakest quarters for [dataset_description]. Context: Data spans multiple quarters with [quarter] and [revenue]. Output: A table of quarter, revenue, growth rate, and a short interpretation. Constraints: Output as a table suitable for import into a slide deck; handle missing quarters gracefully.
4. Customer churn risk indicator Role: You are an Excel data analyst assistant. Task: Build a churn risk indicator from [dataset_description]. Context: Columns include [customer_id], [last_purchase_date], [purchase_frequency], [recency], [monetary_value]. Output: A risk score per customer and a risk-level bucket (low/medium/high) with guidance for actions. Constraints: Use a simple scoring model; export as CSV if requested.
5. Forecast next period revenue Role: You are an Excel data analyst assistant. Task: Forecast next period revenue using a simple model based on [dataset_description]. Context: [date], [revenue], [seasonality], [trend]. Output: Point forecast with confidence interval bounds and a rationale for method chosen. Constraints: If data shows seasonality approximate a seasonal naive or simple exponential smoothing approach and justify choice.
6. Outlier detection in sales data Role: You are an Excel data analyst assistant. Task: Detect and report outliers in the dataset described by [dataset_description]. Context: Columns include [date], [sales], [region], [product]. Output: List of outliers with value, position, and suggested handling (remove, adjust, or flag). Constraints: Use IQR or z score method; explain threshold selection.
7. KPI dashboard blueprint Role: You are an Excel data analyst assistant. Task: Create a blueprint for a KPI dashboard from [dataset_description]. Context: Key metrics include [kpi1], [kpi2], [kpi3], [timeframe]. Output: A dashboard outline including pivot tables, charts, and slicers plus a data model sketch. Constraints: Provide a concise specifications sheet suitable for development.
8. Correlation analysis guidance Role: You are an Excel data analyst assistant. Task: Guide correlation analysis for the dataset described by [dataset_description]. Context: Columns include [variable1], [variable2], [variable3]. Output: List of strong/weak correlations with correlation coefficients and interpretation tips. Constraints: Recommend visualization approach and caveats for causation.
9. Seasonal pattern detection Role: You are an Excel data analyst assistant. Task: Detect seasonal patterns in [dataset_description]. Context: Data spans multiple seasons with [date] and [metric]. Output: Summary of seasonality strength, potential seasonal indices, and recommended adjustments. Constraints: Include a simple seasonal decomposition if feasible.
10. Pareto analysis of defects Role: You are an Excel data analyst assistant. Task: Perform Pareto analysis on defect causes from [dataset_description]. Context: Columns include [defect_type], [count], [date]. Output: Pareto chart data and top contributing defect types with recommendations. Constraints: Show cumulative percentage and threshold insights.
11. Actual vs budget by department Role: You are an Excel data analyst assistant. Task: Compare actuals against budget by department for [dataset_description]. Context: Columns include [department], [actual], [budget], [date]. Output: Gap analysis table and a visual summary. Constraints: Highlight variances over a chosen threshold.
12. Margin analysis by product category Role: You are an Excel data analyst assistant. Task: Analyze gross margin by product category in [dataset_description]. Context: Columns include [category], [cost], [revenue], [quantity]. Output: Margin by category with percent and top/bottom categories. Constraints: Use unit cost where possible and show margins in currency.
13. Inventory turnover calculation Role: You are an Excel data analyst assistant. Task: Calculate inventory turnover for [dataset_description]. Context: Columns include [sku], [opening_inventory], [closing_inventory], [cost_of_goods_sold], [period]. Output: Turnover ratio by sku and overall turnover; include interpretation notes. Constraints: Present in a clean table suitable for reporting.
14. Week-over-week sales trend Role: You are an Excel data analyst assistant. Task: Analyze week over week sales trend in [dataset_description]. Context: Date granularity is weekly; columns include [week_start], [sales], [region]. Output: Trend line summary and a short forecast for the next 4 weeks. Constraints: Provide percentage change and highlight anomalies.
15. Conversion rate funnel from raw data Role: You are an Excel data analyst assistant. Task: Build a conversion rate funnel from [dataset_description]. Context: Columns include [visits], [signups], [purchases], [date]. Output: Funnel stages with conversion rates and drop-offs; include suggestions to improve flow. Constraints: Provide a one sentence action plan.
16. SKU-level profitability review Role: You are an Excel data analyst assistant. Task: Assess profitability by SKU in [dataset_description]. Context: Columns include [sku], [revenue], [cost], [units], [date]. Output: List top and bottom SKUs by profit; include margin percentage and recommended actions. Constraints: Normalize for seasonality if applicable.
17. ROI per marketing campaign Role: You are an Excel data analyst assistant. Task: Compute ROI for each marketing campaign in [dataset_description]. Context: Columns include [campaign], [spend], [revenue], [start_date], [end_date]. Output: ROI per campaign with ranking and a brief interpretation. Constraints: Use net profit and present in a sortable table.
18. Supplier lead time variability analysis Role: You are an Excel data analyst assistant. Task: Analyze supplier lead time variability in [dataset_description]. Context: Columns include [supplier], [lead_time], [date]. Output: Summary stats for lead time by supplier and recommended safety stock. Constraints: Include standard deviation and coefficient of variation.
19. Time-series decomposition components Role: You are an Excel data analyst assistant. Task: Decompose a time series in [dataset_description] into trend, seasonality, and residuals. Context: Data with [date] and [value]. Output: Decomposition results with interpretation and suggested adjustments. Constraints: If possible show equations and short plots using Excel compatible formats.
20. Data quality scoring Role: You are an Excel data analyst assistant. Task: Compute a data quality score for [dataset_description]. Context: Data quality issues include [issue1], [issue2]. Output: Overall quality score, subscores by dimension, and remediation steps. Constraints: Provide a scoring rubric and example calculation.
21. Duplicate detection and cleanup plan Role: You are an Excel data analyst assistant. Task: Identify duplicates in [dataset_description] and propose cleanup steps. Context: Columns include [id], [value], [date]. Output: List of duplicates with counts and suggested deduplication strategy. Constraints: Recommend non destructive cleanup approach.
22. Null value impact assessment Role: You are an Excel data analyst assistant. Task: Assess impact of null values in [dataset_description]. Context: Columns include [field], [rows], [date]. Output: Impact assessment with suggested imputation strategies and risks. Constraints: Propose multiple options with tradeoffs.
23. Data validation rules Role: You are an Excel data analyst assistant. Task: Propose data validation rules for imported data in [dataset_description]. Context: Data comes from [source]. Output: Validation rules list plus sample formulas without exposing sensitive data. Constraints: Ensure maintainability.
24. Dynamic named ranges Role: You are an Excel data analyst assistant. Task: Create dynamic named ranges for a dataset described by [dataset_description]. Context: Data extends over time with [date column]. Output: Names definitions with formulas and a short usage guide. Constraints: Ensure ranges expand automatically.
25. Excel formula best practices Role: You are an Excel data analyst assistant. Task: Provide best practices for crafting robust formulas in [dataset_description]. Context: Common calculations include [metric1], [metric2]. Output: A list of formula patterns with examples and pitfalls to avoid. Constraints: Favor clarity and auditability.
26. Forecast accuracy measurement Role: You are an Excel data analyst assistant. Task: Measure forecast accuracy for [dataset_description]. Context: Contains actuals and forecasts for [periods]. Output: Accuracy metrics such as MAD RMSE MAPE with interpretation. Constraints: Provide a recommendation based on accuracy.
27. Sensitivity analysis setup Role: You are an Excel data analyst assistant. Task: Set up a simple sensitivity analysis for key driver [driver] in [dataset_description]. Context: Range of [low] to [high] values. Output: A table showing impact on outcome and a short narrative. Constraints: Include one scenario with best case and worst case.
28. Scenario analysis for budgets Role: You are an Excel data analyst assistant. Task: Build budget scenarios for [dataset_description]. Context: Variables include [variable1], [variable2]. Output: Scenario table with assumptions and impact on key KPIs. Constraints: Use a clear naming convention for scenarios.
29. Out-of-stock risk alerts Role: You are an Excel data analyst assistant. Task: Produce alerts for potential out-of-stock risk in [dataset_description]. Context: Columns include [date], [inventory], [lead_time], [safety_stock]. Output: List of items at risk and recommended actions with a forecast horizon. Constraints: Prioritize items with high impact.
30. Customer lifetime value projection Role: You are an Excel data analyst assistant. Task: Project customer lifetime value for [dataset_description]. Context: Columns include [customer_id], [purchase_history], [discount_rate], [retention]. Output: LTV by segment with assumptions and a one paragraph interpretation. Constraints: Use a simple model and specify discount rate.
31. Churn prediction using cohort analysis Role: You are an Excel data analyst assistant. Task: Predict churn using cohort analysis for [dataset_description]. Context: Data includes [cohort], [retention], [revenue_per_customer]. Output: Cohort chart data and churn rate by cohort with actionable recommendations. Constraints: Provide clear cohort labeling and interpretation.
32. Sales by region heatmap Role: You are an Excel data analyst assistant. Task: Create a region based heatmap for [dataset_description]. Context: Columns include [region], [sales], [date]. Output: Heatmap data and notes to interpret regional performance. Constraints: Ensure accessibility with color-blind friendly palette.
33. Price elasticity quick test Role: You are an Excel data analyst assistant. Task: Run a quick price elasticity test for [dataset_description]. Context: Data includes [price], [units_sold], [date]. Output: Elasticity estimate with interpretation and recommended pricing actions. Constraints: Use simple linear approximation.
34. Cost per unit trend Role: You are an Excel data analyst assistant. Task: Analyze cost per unit trend in [dataset_description]. Context: Columns include [cost], [units], [date]. Output: Trend summary and a forecast for [period]. Constraints: Normalize for seasonal effects when possible.
35. Employee productivity metrics Role: You are an Excel data analyst assistant. Task: Compute productivity metrics for employees in [dataset_description]. Context: Data includes [employee_id], [hours_worked], [output_units], [date]. Output: Per employee productivity metrics with benchmarks and improvement suggestions. Constraints: Include a summary at team level.
36. Attendance vs output scatter Role: You are an Excel data analyst assistant. Task: Create a scatter plot and analysis for attendance versus output in [dataset_description]. Context: Columns include [employee], [attendance_rate], [output]. Output: Scatter plot outline and correlation insight. Constraints: Provide actionable takeaways.
37. Projected cashflow forecast Role: You are an Excel data analyst assistant. Task: Forecast cashflow for [dataset_description]. Context: Data includes [inflow], [outflow], [period]. Output: Cashflow projection with key drivers and comment. Constraints: Use a simple baseline model and show a sensitivity scenario.
38. Break-even analysis template Role: You are an Excel data analyst assistant. Task: Build a break-even analysis for [dataset_description]. Context: Variables include [fixed_cost], [variable_cost], [price], [units]. Output: Break-even point and a dynamic chart. Constraints: Provide a reusable template.
39. Freight cost optimization prompts Role: You are an Excel data analyst assistant. Task: Optimize freight costs for [dataset_description]. Context: Data includes [shipping_mode], [cost_per_unit], [volume]. Output: Recommended freight mix and savings estimate. Constraints: Show scenario comparisons.
40. Product mix optimization Role: You are an Excel data analyst assistant. Task: Optimize product mix for profitability in [dataset_description]. Context: Columns include [product], [revenue], [cost], [units], [date]. Output: Optimal mix recommendations and expected impact on margin. Constraints: Consider capacity constraints.
41. Supplier performance scoring Role: You are an Excel data analyst assistant. Task: Score supplier performance in [dataset_description]. Context: Data includes [supplier], [delivery_time], [quality], [cost]. Output: Ranked supplier scores and recommended negotiation points. Constraints: Normalize across metrics.
42. Lead conversion timing Role: You are an Excel data analyst assistant. Task: Analyze lead conversion timing for [dataset_description]. Context: Columns include [lead_id], [touchpoints], [conversion_date], [status]. Output: Distribution of conversion times and bottlenecks with optimization tips. Constraints: Provide actionable steps.
43. Email campaign heatmap Role: You are an Excel data analyst assistant. Task: Build a heatmap of email campaign performance in [dataset_description]. Context: Data includes [campaign], [open_rate], [click_rate], [date]. Output: Heatmap plus insights and recommended optimizations. Constraints: Use consistent color scale.
44. Content marketing ROI by channel Role: You are an Excel data analyst assistant. Task: Compute content marketing ROI by channel in [dataset_description]. Context: Columns include [channel], [spend], [revenue], [period]. Output: ROI by channel with ranking and brief interpretation. Constraints: Normalize by audience size where possible.
45. Inventory aging report Role: You are an Excel data analyst assistant. Task: Produce an inventory aging report for [dataset_description]. Context: Data includes [sku], [inventory_level], [aging_days]. Output: Aging buckets with counts and recommended actions. Constraints: Focus on high risk items.
46. Purchase order aging Role: You are an Excel data analyst assistant. Task: Analyze purchase order aging in [dataset_description]. Context: Columns include [po_number], [order_date], [status], [aging_days]. Output: Aging summary and flags for overdue orders. Constraints: Include actionable remediation steps.
47. Freight per unit calculation Role: You are an Excel data analyst assistant. Task: Compute freight cost per unit for [dataset_description]. Context: Data includes [freight_cost], [units], [date]. Output: Cost per unit by category with trend. Constraints: Show currency formatting.
48. Warranty claim analysis Role: You are an Excel data analyst assistant. Task: Analyze warranty claims in [dataset_description]. Context: Columns include [claim_id], [product], [cost], [date]. Output: Claims distribution by product and recommended mitigation actions. Constraints: Include per period totals.
49. Warranty cost projection Role: You are an Excel data analyst assistant. Task: Project warranty costs for [dataset_description]. Context: Data includes [product], [claims], [cost_per_claim], [period]. Output: Forecast with confidence range and drivers. Constraints: Use a simple cost projection method.
50. Returns reason analysis Role: You are an Excel data analyst assistant. Task: Analyze returns reasons in [dataset_description]. Context: Columns include [sku], [returns], [reason], [date]. Output: Reason frequency table and recommended process improvements. Constraints: Highlight top 3 reasons.
51. Customer segmentation prompts Role: You are an Excel data analyst assistant. Task: Segment customers in [dataset_description] based on [vars]. Context: Data includes [customer_id], [spend], [frequency], [recency]. Output: Segmentation labels with profiles and suggested actions. Constraints: Use 4 to 6 segments.
52. RFM segmentation prompts Role: You are an Excel data analyst assistant. Task: Apply RFM segmentation to [dataset_description]. Context: Columns include [customer_id], [recency], [frequency], [monetary]. Output: RFM segments with counts and targeted offers. Constraints: Provide a short interpretation per segment.
53. Data cleaning checklist Role: You are an Excel data analyst assistant. Task: Produce a data cleaning checklist for the dataset described by [dataset_description]. Context: Data quality issues include [issue1], [issue2]. Output: Step by step cleanup plan with owners and timelines. Constraints: Prioritize irreversible actions after backup.
54. Rolling average trend Role: You are an Excel data analyst assistant. Task: Compute a rolling average trend for [dataset_description]. Context: Data includes [date], [value], [window]. Output: Rolling average series and interpretation notes. Constraints: Choose a reasonable window length.
55. Moving average crossover signals Role: You are an Excel data analyst assistant. Task: Identify moving average crossover signals in [dataset_description]. Context: Columns include [date], [value]. Output: Crossover events and potential trading or operational signals. Constraints: Show both short and long window results.
56. Median and IQR data summary Role: You are an Excel data analyst assistant. Task: Provide a robust data summary using median and IQR for [dataset_description]. Context: Data includes [values]. Output: Statistical summaries and outlier insights. Constraints: Include a compact table.
57. Histogram distribution check Role: You are an Excel data analyst assistant. Task: Check distribution for [dataset_description]. Context: Data includes [column]. Output: Histogram bin counts and distribution interpretation. Constraints: Include recommendations for normalization if needed.
58. Outlier-adjusted revenue forecast Role: You are an Excel data analyst assistant. Task: Produce an outlier-adjusted revenue forecast for [dataset_description]. Context: Data includes [revenue], [outliers], [date]. Output: Forecast with and without outlier adjustments and a short rationale. Constraints: Explain method of outlier treatment.
59. Data validation for imports Role: You are an Excel data analyst assistant. Task: Propose data validation steps for imported data in [dataset_description]. Context: Data comes from [source]. Output: Validation rules list plus sample formulas without exposing sensitive data. Constraints: Ensure maintainability.
60. Dynamic dashboards with slicers Role: You are an Excel data analyst assistant. Task: Design a dynamic dashboard workflow for [dataset_description]. Context: Data model includes [tables], [relationships]. Output: Dashboard structure with slicers and interactivity notes. Constraints: Provide a reusable blueprint.
61. Excel data model relationships Role: You are an Excel data analyst assistant. Task: Define data model relationships for [dataset_description]. Context: Data includes [fact], [dimension], [lookup]. Output: Relationship diagram description and sample query paths. Constraints: Keep model scalable.
62. DAX-like KPI approximation in Excel Role: You are an Excel data analyst assistant. Task: Approximate DAX style KPIs using Excel formulas for [dataset_description]. Context: KPIs include [kpi1], [kpi2]. Output: A list of equivalent Excel formulas and a validation checklist. Constraints: Prioritize readability.
63. Gradient-free regression outline Role: You are an Excel data analyst assistant. Task: Outline a gradient-free regression approach for [dataset_description]. Context: Data includes [x], [y]. Output: Stepwise approach with simple formulas and example interpretation. Constraints: Keep simple and explain assumptions.
64. Column labeling best practices Role: You are an Excel data analyst assistant. Task: Propose labeling conventions for columns in [dataset_description]. Context: Data will be used across dashboards and models. Output: Labeling scheme with rules and examples. Constraints: Align with data governance.
65. Data provenance notes generator Role: You are an Excel data analyst assistant. Task: Generate data provenance notes for [dataset_description]. Context: Data sources include [source1], [source2]. Output: Provenance summary with data lineage and maintenance notes. Constraints: Include last update date.
66. Pivot table best practices Role: You are an Excel data analyst assistant. Task: Provide best practices for pivot tables in [dataset_description]. Context: Data includes [dimensions], [measures]. Output: Pivot table guidelines and a quick layout plan. Constraints: Emphasize readability and drill-down paths.
67. Slicer-driven cross-tab analysis Role: You are an Excel data analyst assistant. Task: Demonstrate slicer driven cross tab analysis for [dataset_description]. Context: Data model supports slicers on [dimension]. Output: Step by step instructions and example results. Constraints: Ensure slicers control multiple visuals.
68. Forecast scenario storytelling Role: You are an Excel data analyst assistant. Task: Create a narrative around forecast scenarios for [dataset_description]. Context: Includes multiple scenarios such as base case, optimistic, pessimistic. Output: Brief executive summary with visuals guidance. Constraints: Use plain language for non-technical audiences.
69. Cash burn rate analysis Role: You are an Excel data analyst assistant. Task: Analyze cash burn rate in [dataset_description]. Context: Data includes [cash_in], [cash_out], [date]. Output: Burn rate trend and recommendations to improve liquidity. Constraints: Include a simple forecast.
70. Accounts receivable aging Role: You are an Excel data analyst assistant. Task: Build an AR aging report from [dataset_description]. Context: Data includes [customer], [invoice_date], [due_date], [balance]. Output: AR aging buckets with totals and action notes. Constraints: Highlight overdue items.
71. Inventory reorder point calculation Role: You are an Excel data analyst assistant. Task: Calculate reorder points for inventory in [dataset_description]. Context: Data includes [sku], [lead_time], [demand_rate], [safety_stock]. Output: Reorder point per SKU with rationale. Constraints: Include a sensitivity note.
72. Supplier rating rubric Role: You are an Excel data analyst assistant. Task: Create a supplier rating rubric in [dataset_description]. Context: Metrics include [delivery], [quality], [cost], [lead_time]. Output: Weighted rubric and sample scores. Constraints: Make it actionable.
73. Shipping cost per SKU Role: You are an Excel data analyst assistant. Task: Compute shipping cost per unit for [dataset_description]. Context: Data includes [sku], [shipping_cost], [units]. Output: Cost per unit per SKU and recommended reductions. Constraints: Normalize by weight or volume when possible.
74. Customer feedback sentiment scoring Role: You are an Excel data analyst assistant. Task: Score customer feedback sentiment in [dataset_description]. Context: Data includes [feedback_text], [rating], [date]. Output: Sentiment score by segment and actionable insights. Constraints: Provide a reproducible scoring approach.
75. Net promoter score follow-up Role: You are an Excel data analyst assistant. Task: Analyze NPS and suggest improvements for [dataset_description]. Context: Data includes [nps_score], [region], [date]. Output: NPS trend and recommended actions by region. Constraints: Include confidence interpretation.
76. Sales channel profitability Role: You are an Excel data analyst assistant. Task: Evaluate profitability by sales channel in [dataset_description]. Context: Columns include [channel], [cost], [revenue], [units]. Output: Channel profitability table and recommended channel mix. Constraints: Normalize by channel size.
77. Channel mix trend analysis Role: You are an Excel data analyst assistant. Task: Analyze channel mix over time in [dataset_description]. Context: Data includes [date], [channel], [revenue]. Output: Trend by channel with interpretation notes. Constraints: Include a forecast for next period.
78. Break-even sensitivity table Role: You are an Excel data analyst assistant. Task: Build a break-even sensitivity table for [dataset_description]. Context: Variables include [price], [cost], [volume]. Output: Sensitivity table with break-even points under scenarios. Constraints: Exportable to CSV.
79. Revenue per employee calculator Role: You are an Excel data analyst assistant. Task: Calculate revenue per employee for [dataset_description]. Context: Data includes [employee], [revenue], [date]. Output: Revenue per employee and team benchmark. Constraints: Include top and bottom performers.
80. Product return rate analysis Role: You are an Excel data analyst assistant. Task: Analyze product return rate in [dataset_description]. Context: Data includes [sku], [returns], [units], [date]. Output: Return rate by product and recommended actions. Constraints: Compare against industry benchmarks if available.
81. Warranty claim rate by product Role: You are an Excel data analyst assistant. Task: Analyze warranty claim rate by product in [dataset_description]. Context: Columns include [product], [claims], [sales]. Output: Claim rate by product with action suggestions. Constraints: Highlight products with elevated risk.
82. Lead time by supplier heatmap Role: You are an Excel data analyst assistant. Task: Create a heatmap of supplier lead times in [dataset_description]. Context: Data includes [supplier], [lead_time], [date]. Output: Visual heatmap and interpretation notes. Constraints: Ensure color scale is intuitive.
83. Month-over-month revenue delta Role: You are an Excel data analyst assistant. Task: Compute month-over-month revenue delta in [dataset_description]. Context: Data includes [date], [revenue]. Output: Delta values and interpretation. Constraints: Include a note on seasonality.
84. Data completeness scorecard Role: You are an Excel data analyst assistant. Task: Build a data completeness scorecard for [dataset_description]. Context: Data fields and current statuses are provided. Output: Scorecard with gaps and remediation steps. Constraints: Include a dashboard friendly summary.
85. Data normalization rules Role: You are an Excel data analyst assistant. Task: Propose data normalization rules for [dataset_description]. Context: Data includes [field types]. Output: Normalization plan with examples and checks. Constraints: Ensure comparability across datasets.
86. Currency conversion consistency Role: You are an Excel data analyst assistant. Task: Check currency conversion consistency for [dataset_description]. Context: Data includes [currency], [exchange_rate], [amount]. Output: Consistency report with recommended practices. Constraints: Include a fallback plan for missing rates.
87. Sparklines and mini charts Role: You are an Excel data analyst assistant. Task: Integrate sparklines and mini charts for key metrics in [dataset_description]. Context: Data includes [dates] and [values]. Output: Guidance for embedding visuals and a sample layout. Constraints: Keep visuals lightweight.
88. Confidence interval quick calc Role: You are an Excel data analyst assistant. Task: Calculate a quick confidence interval for a metric in [dataset_description]. Context: Data includes [sample], [mean], [std_dev]. Output: Confidence interval formula and result. Constraints: Use a standard normal assumption.
89. Correlation vs causation guardrails Role: You are an Excel data analyst assistant. Task: Provide guardrails to avoid misinterpreting correlations in [dataset_description]. Context: Data includes multiple variables. Output: List of common pitfalls and recommended checks. Constraints: Include example interpretations.
90. Scorecard legend clarity guide Role: You are an Excel data analyst assistant. Task: Create a legend clarity guide for scorecards in [dataset_description]. Context: Visuals include colors and symbols. Output: Legend descriptions and a quick checklist for consistency. Constraints: Make legend unambiguous.
91. Data storytelling thumbnail prompts Role: You are an Excel data analyst assistant. Task: Generate prompts for crafting data driven storytelling thumbnails for dashboards in [dataset_description]. Context: Audience is non technical. Output: List of thumbnail prompts and suggested visuals. Constraints: Align with executive briefing style.
92. Excel automation readiness check Role: You are an Excel data analyst assistant. Task: Assess readiness for Excel automation in [dataset_description]. Context: Current workflows include [manual_step1], [manual_step2]. Output: Automation readiness score and a stepwise plan. Constraints: Prioritize high impact automations.
93. Audit trail for data edits Role: You are an Excel data analyst assistant. Task: Propose an audit trail plan for data edits in [dataset_description]. Context: Data changes occur by [user] and [date]. Output: Audit log structure and change tracking rules. Constraints: Ensure privacy and compliance.
94. Scenario-based board briefing Role: You are an Excel data analyst assistant. Task: Prepare a scenario based briefing for board in [dataset_description]. Context: Include base, optimistic, and pessimistic scenarios. Output: Briefing outline with visuals and recommendations. Constraints: Use concise language.
95. What-if analysis for pricing Role: You are an Excel data analyst assistant. Task: Perform what-if analysis for pricing in [dataset_description]. Context: Variables include [price], [demand], [cost]. Output: Price vs demand table and recommended price points. Constraints: Show impact on margin.
96. KPI calculation audit Role: You are an Excel data analyst assistant. Task: Audit KPI calculations in [dataset_description]. Context: Metrics include [kpi1], [kpi2]. Output: List of calculation steps, validation checks, and fixes. Constraints: Ensure reproducibility.
97. Data import error diagnosis Role: You are an Excel data analyst assistant. Task: Diagnose import errors in [dataset_description]. Context: Errors occur during data ingestion. Output: Likely causes list and fixes with a test plan. Constraints: Prioritize non destructive checks.
98. Pivot chart storytelling prompts Role: You are an Excel data analyst assistant. Task: Generate prompts for pivot chart based storytelling in [dataset_description]. Context: Data used for executive summary. Output: Pivot chart prompts with narrative lines. Constraints: Align with key decisions.
99. Forecast vs actual variance drilldown Role: You are an Excel data analyst assistant. Task: Drill down into forecast vs actual variance in [dataset_description]. Context: Data includes [period], [forecast], [actual]. Output: Variance breakdown by driver and recommended corrective actions. Constraints: Provide actionable steps.
100. Excel risk assessment checklist Role: You are an Excel data analyst assistant. Task: Create a risk assessment checklist for Excel based analysis in [dataset_description]. Context: Potential risks include data integrity governance automation gaps. Output: Comprehensive checklist with owner and frequency. Constraints: Ensure applicability across projects.

Best practices

General guidance to improve accuracy reuse and safety of Claude prompts for Excel analysis includes documenting data context validating outputs and iterating prompts for consistency.

Common Mistakes to Avoid

Avoid vague prompts assuming Claude understands your data avoid asking for proprietary or sensitive data and avoid over asking for unverified projections.

Related resources

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

• AI Prompts Library
• Claude Prompts
• AI workflow simulator
• Cross-model AI workflows with Claude
• Claude data analysis prompts
• How to connect AI to Excel
• Reduce manual Excel reporting
• Outlook emails and Excel customer records
• Excel staffing analysis use case

FAQ

What is the best way to use these prompts for Excel Analysis?

Use the prompts to guide Claude through concrete analyses with clear data context and output expectations. Verify outputs in Excel and iterate prompts as needed.

Can I customize prompts for different datasets

Yes customize placeholders like [dataset_description] and [date] to fit your dataset and replace [period] with your reporting horizon.

What formats can Claude return data in?

Request structured outputs such as CSV JSON or a clearly formatted table that can be pasted into Excel. Always specify the output format.

Do these prompts guarantee accuracy?

No prompts do not guarantee accuracy; they guide analysis. Always validate Claude outputs against your data and apply human review for critical decisions.

Are there prompts for dashboard creation

Yes many prompts cover KPI dashboards pivot tables slicers and visual storytelling and can be adapted to your data model.