100 Best Claude Excel Data Analysis Prompts for Shopify Stores

Direct Answer

The best Claude prompts for Excel data analysis for Shopify stores are a practical library of tasks that transform Shopify exports into actionable insights. Use these prompts to drive sales, optimize inventory, and sharpen marketing ROI from Excel-based analyses. Each prompt is designed to be plug-and-play, with clear placeholders and outputs.

Introduction

This page is a practical prompt library for Excel data analysis tailored to Shopify stores. It helps store owners and analysts generate precise, Excel-ready analyses that improve decision making.

Whether you manage a small shop or a high-volume store, these Claude prompts provide step by step instructions, data requirements, and expected outputs to streamline analytics in Excel.

How to Use These Claude Prompts

• Replace placeholders like [industry], [audience], and [constraints] with your data and goals.
• Add constraints to outputs to keep results concise and actionable for Excel dashboards.
• Request outputs in formats that Excel can ingest, such as CSV or tabular data blocks, and verify results by cross-checking totals or trends.
• When possible, specify the date range and data source to ensure reproducibility.
• Keep a running set of prompts as templates for common tasks like revenue by channel or inventory turnover.
• implements placeholders for quick adaptation.

100 Best Claude Excel Data Analysis Prompts for Shopify Stores

1. Sales by Channel Deep Dive: You are a data analyst for a Shopify store. Task: analyze sales by channel (online store, social, marketplaces) using an Excel export with columns Date, Channel, Product, Units Sold, Revenue, Cost. Role: you. Context: provide a pivot-ready summary with channel profitability. Output: a tabular summary with total revenue, units, average order value by channel, and top products by channel. Constraints: include a one-line actionable insight per channel and export a compact chart-ready table in CSV format.
2. Top Products by Revenue: Role: Data analyst. Task: identify top 10 products by revenue from the given Shopify sales export with fields Date, Product, Revenue. Context: use all-time data and latest month. Output: a ranked list of products with revenue, units sold, and revenue share. Format: bullet list plus a compact CSV block for import into Excel.
3. Inventory Turnover: You are an inventory analyst. Task: compute inventory turnover ratio by product using columns Product, StockOnHand, UnitsSold, CostOfGoodsSold, Period. Context: provide turnover by product for the last 90 days. Output: a ranked table with turnover ratio, days to turnover, and replenishment suggestions. Output format: Excel-ready table and a one-sentence action per product.
4. Gross Margin by Product: Role: finance analyst. Task: calculate gross margin per product using fields Product, Revenue, CostOfGoodsSold. Context: include margin percentage and highlight products below a 30 percent margin. Output: a sorted table with product, revenue, cost, gross margin, and margin rank. Constraints: flag items to consider for price adjustments.
5. COGS vs. Revenue Timeline: You are an operations analyst. Task: create a timeline view of costs and revenue by month for Shopify store. Data: fields Month, Revenue, CostOfGoodsSold, AdvertisingCost. Output: a stacked column timeline with annotations for major cost spikes. Output: Excel-ready data plus a chart plan.
6. Marketing ROI by Channel: Role: marketing analyst. Task: compute ROI by channel using fields Channel, RevenueAttribution, AdvertisingCost, Date. Context: use last three months. Output: table of channel ROI, payback period, and recommendations. Format: concise bullet list and an Excel-ready CSV block.
7. AOV by Product Category: You are a retail data analyst. Task: calculate average order value by product category from Shopify export with fields Date, Category, Revenue, Orders. Context: last 12 months. Output: a category table with AOV, trend, and top category. Output: Excel-ready sheet and a chart-ready summary.
8. Cohort Analysis Setup: Role: data scientist. Task: build a basic cohort analysis from customer purchase data with fields CustomerID, OrderDate, Revenue. Context: cohort by month of first order. Output: retention curve and revenue per cohort in Excel-ready format.
9. Seasonality Insights: You are an analytics engineer. Task: detect seasonality in monthly revenue and orders from Shopify data with fields Month, Revenue, Orders. Context: include SSA/seasonal pattern notes. Output: a short interpretation plus a ready-to-plot CSV for Excel.
10. Return Rate Analysis: Role: operations analyst. Task: compute product return rate by product using fields Product, Returns, Orders. Context: last quarter. Output: ranked list of products by return rate with potential causes and action steps.
11. Customer Lifetime Value: You are a data analyst. Task: estimate CLV using repeat purchase value and order frequency from fields CustomerID, InvoiceDate, Revenue. Context: last 24 months. Output: CLV per customer segment with recommended actions in Excel-ready table.
12. Lead-to-Customer Conversion: Role: growth analyst. Task: analyze conversion from leads to customers using fields LeadDate, OrderDate, Revenue. Context: last six months. Output: funnel stages, conversion rate, and lag time.
13. Stockout Risk Report: You are an inventory planner. Task: identify stockouts risk by product using StockOnHand, UnitsSold, ReorderPoint. Context: last 60 days. Output: risk score per product and reorder recommendations, in an Excel-ready sheet.
14. Pricing Elasticity Scan: Role: pricing analyst. Task: assess price sensitivity using historical price changes and demand signals in the data. Context: include price and units sold. Output: elasticity estimates by product with recommended price bands.
15. Channel Profitability Mix: You are a business analyst. Task: compare profitability mix across channels using Revenue, Cost, and Channel. Context: last 12 months. Output: 2x2 profitability matrix per channel and a top-line recommendation.
16. Refunds vs Revenue: Role: finance analyst. Task: analyze refunds relative to revenue by product. Data: Product, Revenue, Refunds. Context: last quarter. Output: refund rate by product and impact on margins with remediation notes.
17. Ship Cost Optimization: You are an operations analyst. Task: evaluate shipping costs per order and propose optimizations using fields OrderID, ShippingCost, Destination, Weight. Context: last 1000 orders. Output: cost-saving recommendations with Excel-ready calculations.
18. Tax Compliance Check: Role: compliance analyst. Task: verify tax codes and collected tax by jurisdiction from Shopify tax data. Context: last 6 months. Output: jurisdiction-wise tax totals and reconciliation notes in Excel-ready format.
19. Discount Impact Analysis: You are a promotions analyst. Task: measure discount impact on revenue and units sold per promotion. Data: PromotionID, DiscountAmount, Revenue, UnitsSold. Context: all-time data. Output: ROI by promotion and optimization suggestions.
20. Product Performance Heatmap: Role: data analyst. Task: generate a heatmap of product performance by region using fields Product, Region, Revenue, UnitsSold. Context: last 12 months. Output: ranked heatmap data suitable for Excel.
21. Inventory Valuation: You are an inventory analyst. Task: compute current inventory valuation by product using StockOnHand and Cost. Context: real-time snapshot. Output: valuation report with total and per-product breakdown in Excel-ready format.
22. Basket Size Forecast: Role: forecasting analyst. Task: forecast next month average basket size using past 18 months of order data. Output: forecast value, confidence interval, and key drivers.
23. Marketplace Performance: You are a data analyst. Task: compare Shopify store performance against marketplaces with fields Channel, Revenue, Orders. Context: last six months. Output: performance delta table and insights.
24. SKU Rationalization: Role: product analytics. Task: identify underperforming SKUs for potential discontinuation using sales, margin, and stock data. Output: list of SKUs with rationale and recommended actions.
25. Cohort Revenue by Month: You are a data analyst. Task: compute revenue by month for each customer cohort defined by first order month. Context: last 24 months. Output: cohort table and recommendations in Excel-ready format.
26. Promo Lift by Channel: Role: marketing data analyst. Task: measure promotional lift by channel using PromotionID, Channel, Revenue, NormalizedRevenue. Context: recent campaigns. Output: lift percentage per channel and action notes.
27. Gross Margin Customizer: You are a finance analyst. Task: create a customizable gross margin report by product with adjustable tax and freight assumptions. Context: last 12 months. Output: margin table with scenario sliders in Excel-ready format.
28. A/B Test Synthesis: Role: data scientist. Task: synthesize A/B test results from experiments stored in tabs with metrics and significance. Output: concise verdict and recommended next steps.
29. Order Delay Analysis: You are an operations analyst. Task: analyze order delays using OrderDate, ShipDate, and Destination. Context: last 90 days. Output: delay distribution and top delay causes with mitigation steps.
30. Promotions Calendar: Role: marketer. Task: build a promotions calendar from PromotionID, StartDate, EndDate, Channel. Output: calendar view and impact plan for Excel.
31. Currency Impact Check: You are an analytics engineer. Task: adjust revenue figures for currency fluctuations using fields RevenueLocal, Currency, ExchangeRate. Context: last 12 months. Output: reconciled revenue in base currency and volatility notes.
32. Customer Segmentation: Role: data analyst. Task: segment customers by RFM using fields CustomerID, LastPurchaseDate, TotalRevenue, Orders. Context: last 24 months. Output: segmentation table with actionable insights.
33. Product Bundling Opportunity: You are a product analytics specialist. Task: identify bundling opportunities by cross-selling products with complementary sales data. Output: recommended bundles and projected impact in Excel-ready format.
34. Return Reason Trends: Role: operations analyst. Task: analyze return reasons by product over time using Returns, Reason, Product. Context: last 12 months. Output: trend table and corrective actions.
35. Inventory Aging: You are an inventory analyst. Task: rank stock by aging days using StockOnHand and LastSoldDate. Context: last 180 days. Output: aging buckets and replenishment notes.
36. Lead Time Analysis: Role: logistics analyst. Task: compute supplier lead time variance using OrderDate, ShipmentDate, Supplier. Context: last 6 months. Output: lead time distribution and improvement plan.
37. Taxable Revenue by State: You are a finance analyst. Task: break out taxable revenue by state using TaxCollected, Revenue, State. Context: last quarter. Output: state tax totals and compliance checklist.
38. Acquisition Channel Map: Role: growth strategist. Task: map acquisition cost and revenue by channel against benchmarks. Context: last 12 months. Output: channel map with priorities and budget notes.
39. Seasonal Stock Replenish: You are an inventory planner. Task: plan replenishment for seasonal demand using past 3 years of monthly demand per product. Output: replenishment suggestions and safety stock levels.
40. LTV by Acquisition Src: Role: marketing analyst. Task: estimate lifetime value by acquisition source using CustomerID, Source, Revenue, Orders. Context: last 24 months. Output: LTV table by source with recommendations.
41. Variant Performance Audit: You are a data auditor. Task: audit product variant performance by color/size using variants data. Output: accuracy check and discovery notes for Excel.
42. Churn Risk Flags: Role: customer success analyst. Task: identify churn risk indicators using LastPurchaseDate, CustomerStatus, Revenue. Context: last 12 months. Output: risk flags and retention actions in Excel-ready format.
43. Refund Trend Dissection: You are a finance analyst. Task: dissect refund trends by product cohort using Refunds, Revenue, Product. Context: last 12 months. Output: cohort refund rates and remediation steps.
44. Campaign Attribution: Role: analytics lead. Task: attribute revenue to marketing campaigns using CampaignID, Revenue, Clicks, Conversions. Context: last 6 months. Output: attribution model and top drivers in Excel-ready format.
45. SKU Profitability Matrix: You are a product analytics specialist. Task: build a profitability matrix by SKU with Revenue, Cost, Margin. Context: last 12 months. Output: matrix with recommended SKUs to promote.
46. Order Value Momentum: Role: data analyst. Task: identify momentum in average order value over time using Date, Revenue, Orders. Context: last 18 months. Output: trajectory and forecast with confidence.
47. Stock Level Alerts: You are an inventory analyst. Task: set up alert thresholds for StockOnHand by product. Context: real-time data feed. Output: alert list and automated actions plan for Excel.
48. Shipping Zone Analysis: Role: logistics analyst. Task: evaluate shipping costs by zone using Zone, ShippingCost, Orders. Context: last 12 months. Output: zone-cost map and optimization recommendations.
49. Bulk Discount Scenarios: You are a pricing strategist. Task: simulate bulk discount scenarios in Excel using fields Product, BasePrice, Discount, Demand. Context: next quarter. Output: recommended discount levels and expected impact.
50. Cart Abandonment Signals: Role: ecommerce analyst. Task: identify signals for cart abandonment using CartValue, TimeOnSite, Abandoned. Context: last 90 days. Output: risk factors and recovery plan.
51. Payment Method Insights: You are a data analyst. Task: compare revenue by payment method using PaymentMethod, Revenue, Fees. Context: last 6 months. Output: method performance table and optimization tips.
52. Product Life Cycle: Role: product analytics. Task: map products through life cycle stages using LaunchDate, Revenue, UnitsSold. Context: last 24 months. Output: lifecycle chart and action plan.
53. Slow-Minute Trend Check: You are a data scientist. Task: identify sudden short-term spikes or drops in revenue within a minute-level view if available. Output: anomaly notes and follow-up checks.
54. Checkout Flow Bottlenecks: Role: UX analyst. Task: analyze checkout drop-off using funnel data with steps, time, and completion. Context: last 60 days. Output: bottleneck points and improvement suggestions.
55. Refund Window Analysis: You are a finance analyst. Task: analyze refunds by purchase window using OrderDate, RefundDate, Revenue. Context: last 6 months. Output: refund timing distribution and policy notes.
56. Inventory Pulse Metrics: Role: inventory manager. Task: deliver daily pulse metrics for stock health using StockOnHand, Turns, ReorderPoint. Output: concise dashboard-ready values.
57. Customer Segments by AOV: You are a data analyst. Task: segment customers by AOV and recency. Context: last 12 months. Output: segment table with recommended actions.
58. Cross-Sell Readiness: Role: merchandising analyst. Task: identify cross-sell opportunities using purchase history; output recommended bundles in Excel-ready format.
59. Loyalty Program Impact: You are a marketing analyst. Task: measure loyalty program impact on repeat purchases using CustomerID, LoyaltyPoints, Revenue. Context: last 12 months. Output: lift in repeat purchases and recommendations.
60. Channel Cadence Plan: Role: growth strategist. Task: plan outreach cadence by channel based on response rates and revenue. Context: last six campaigns. Output: cadence timeline and budget notes.
61. Product Gap Analysis: You are a product analyst. Task: identify gaps by comparing expected vs actual sales per product. Context: last 12 months. Output: gap list with root causes and actions.
62. Fulfillment SLA Review: Role: operations manager. Task: assess fulfillment SLA adherence using ShipDate, DeliveryDate, promised SLA. Output: adherence score and improvement plan.
63. Customer Demographics: You are a data analyst. Task: profile customers by demographics using available fields. Context: last 24 months. Output: demographic clusters and marketing implications.
64. Refund Reason Cleanup: Role: data quality engineer. Task: clean and categorize refund reasons for consistency. Output: standardized reason codes and mapping.
65. Discount Stewardship: You are a pricing auditor. Task: verify discount usage against policy with fields PromotionID, Discount, Product. Context: last 12 months. Output: policy violations and corrective actions.
66. Forecast Accuracy Check: Role: forecasting analyst. Task: compare forecast to actuals by month. Output: accuracy metrics and adjustment notes.
67. Vendor Cost Trends: You are a procurement analyst. Task: analyze supplier cost trends using Supplier, Cost, Date. Context: last 24 months. Output: cost trend chart and negotiation notes.
68. Customs and Duties Impact: Role: compliance analyst. Task: assess impact of duties on landed cost using Product, DutyRate, Country. Output: adjustments and reporting notes.
69. Cart Value Segmentation: You are a data analyst. Task: segment carts by value to tailor promotions using CartValue, Products, Revenue. Context: last 60 days. Output: value-based segmentation with recommended offers.
70. Product Margin by Region: Role: finance analyst. Task: compute margin per product by region using Region, Product, Revenue, Cost. Context: last 12 months. Output: regional margin table and action items.
71. AEO Readiness Check: You are an analytics engineer. Task: prepare data and prompts for automated extraction for Excel analysis. Output: data hygiene checklist and prompt optimizations.
72. Returns Benchmarking: Role: operations analyst. Task: benchmark return rates against industry norms using product and category. Output: benchmark table and improvement plan.
73. SKU Flagging System: You are a data architect. Task: implement a simple SKU flag system for performance indicators. Output: flag rules and example flags in Excel-ready form.
74. Promotion Overlap Check: Role: marketing analyst. Task: detect overlapping promotions affecting revenue using PromotionID, Date, Revenue. Output: overlap report and conflict resolution steps.
75. Seasonal Marketing Calendar: You are a marketing planner. Task: generate seasonal marketing calendar aligned with revenue patterns. Output: calendar and budget notes in Excel.
76. Shipping Cost Benchmark: Role: logistics analyst. Task: benchmark shipping costs against industry peers by region. Output: benchmark table and optimization steps.
77. Conversion Lag Analysis: You are a growth analyst. Task: measure lag between ad clicks and purchases using Date, Clicks, Orders. Context: last 90 days. Output: lag distribution and implications for attribution.
78. Product Profit Explorer: Role: product analyst. Task: explore per-product profit drivers with Revenue, Cost, and Fees. Output: profit summary and recommendations.
79. Inventory Reorder Planner: You are an inventory planner. Task: generate reorder quantities using stock on hand, lead time, and demand forecast. Output: reorder list with safety stock.
80. Checkout Experience Metrics: Role: UX data analyst. Task: measure checkout steps completion rate using funnel data and time. Output: bottlenecks and improvement plan.
81. SKU Performance Clustering: You are a data scientist. Task: cluster SKUs by performance metrics for portfolio review. Output: clusters with recommended actions.
82. Advertising Spend Allocation: Role: media planner. Task: allocate ad spend by channel to maximize revenue using recent performance data. Output: spend plan and expected ROI.
83. Refund Timing Optimization: You are a finance analyst. Task: reduce refund delays by identifying late refunds and causes. Output: process improvements and timeline targets.
84. Customer Feedback Synthesis: Role: customer insights analyst. Task: summarize feedback by product using sentiment tags and key themes. Output: insights in Excel-friendly format.
85. Data Hygiene Audit: You are a data quality engineer. Task: audit Shopify exports for completeness and consistency. Output: issues list and remediation steps.
86. Order Frequency Trend: Role: data analyst. Task: analyze order frequency per customer over time. Output: trend line and segmentation actions.
87. Market Demand Signals: You are a demand planner. Task: identify rising demand signals from sales data. Output: top rising products and recommended actions.
88. Sales Forecast Scenarios: Role: forecaster. Task: generate multiple forecast scenarios for next quarter with confidence levels. Output: scenario table and actions.
89. Discount Leakage Check: You are a pricing auditor. Task: detect discount leakage across channels from PromotionID, Revenue, Discount. Output: leakage report and fixes.
90. Customer Win-Back Plan: Role: customer success analyst. Task: propose win-back campaigns for lapsed customers using LastPurchaseDate, EmailResponded, Revenue. Output: plan with messaging and timing.
91. Product Lifecycle Watch: You are a product economist. Task: watch product lifecycle stage transitions with Revenue and UnitsSold over time. Output: lifecycle signals and actions.
92. Shipping Delay Root Cause: Role: logistics analyst. Task: root cause analysis for shipping delays using ShipDate, Destination, Carrier. Output: root causes and mitigation steps.
93. Return Rate by Category: You are a data analyst. Task: analyze return rate by product category. Context: last 12 months. Output: category ranking and action plan.
94. Sales by Time of Day: Role: analytics engineer. Task: correlate sales by hour of day with promotions. Output: hourly revenue chart data and recommendations.
95. A/B Test Executor: You are a data scientist. Task: design simple A/B tests for price or presentation and track outcomes using a shared sheet. Output: test design and success criteria.
96. Data Export Checklist: Role: data engineer. Task: prepare an export-ready data extract for Excel analysis with metadata. Output: checklist and template export plan.
97. Inventory Compliance: You are an inventory controller. Task: verify stock records against physical counts. Output: discrepancy report and reconciliation steps.
98. Demand Forecast Validation: Role: forecasting analyst. Task: validate forecast against actuals and adjust methods. Output: validation metrics and updated model notes.
99. Fraud Signal Audit: You are a risk analyst. Task: scan orders for common fraud signals using order value, shipping address, and velocity. Output: risk flags and response plan.
100. Consolidated Analytics Plan: Role: analytics lead. Task: outline a consolidated Excel analytics plan for Shopify data with recommended metrics and dashboards. Output: plan and implementation steps in Excel-ready format.

Best Practices

• Convert all date fields to a standard format before analysis to ensure accurate aggregations.
• Use consistent currency and tax rules when comparing revenue across regions.
• Document assumptions in Excel alongside the results to aid review and reuse.
• Keep prompts modular so you can mix and match analyses for dashboards.
• Test prompts on a small data sample before applying to full exports to catch edge cases.

Common Mistakes to Avoid

• Assuming all Shopify exports have identical column names; always confirm column names before running prompts.
• Overloading prompts with too many outputs; prefer focused results that fit Excel dashboards.
• Neglecting data quality; run a quick cleanse step before analysis.
• Forgetting to specify the date range or currency, leading to misleading comparisons.
• Copying prompts without adjusting placeholders, causing mismatched data contexts.

FAQ

What exactly is Excel data analysis for Shopify stores used for?

It is used to extract sales, inventory, and marketing insights from Shopify exports and present them in Excel dashboards for better decision making.

Do these prompts require Excel macros or extra tools?

No, the prompts are designed to work with standard Excel features and CSV data imports; no macros are required.

Can I customize prompts for a specific store niche?

Yes, replace placeholders with your niche and data specifics, and adjust outputs to match your business questions.

Are these prompts suitable for beginners?

Yes, most prompts use clear steps and outputs; start with simpler analyses like Sales by Channel and grow to more advanced reports.

How do I verify the outputs from Claude prompts?

Cross-check totals, validate with known baselines, and reproduce results in a separate Excel sheet to ensure consistency.

Can I use these prompts with Shopify data exports directly?

Yes, these prompts assume standard Shopify export fields and can be adapted to your exact export schema.