100 Best Claude Excel Data Analysis Prompts for Amazon Sellers

Direct Answer

The best Claude prompts for Excel data analysis for Amazon sellers are a curated collection of tasks that guide you through sales, inventory, pricing, and performance insights directly from Excel data. Use these prompts to generate actionable dashboards, identify profit opportunities, and optimize operations. They are designed to be copyable and immediately usable without extra context.

Introduction

This page is a practical prompt library for Excel data analysis tailored to Amazon sellers. It helps optimize listings, inventory, pricing, and profitability by turning spreadsheet data into clear insights.

The prompts assume you have an Excel workbook with standard ecommerce data columns and require no additional tools beyond Claude to produce structured outputs.

How to Use These Claude Prompts

• Replace placeholders like [min_units], [period], and [n_months] with your actual values before running. Add any constraints you need to ensure outputs fit your data quality and business rules. Request formats such as tables, CSV-ready data, or JSON snippets to fit downstream processes. Validate outputs by cross-checking a subset of results against your source data. Save prompts as templates for reuse across products and quarters.

100 Best Claude Excel Data Analysis Prompts for Amazon Sellers

1. Sales by SKU trend: You are a data analyst for an Amazon seller. Task: analyze quarterly sales by SKU from an Excel table named [data]. Context: the sheet includes columns SKU, date, units_sold, revenue, returns, ad_spend. Output: a clean summary table with SKU, quarter, units_sold, revenue, returns rate, and ROI. Format: plain table with rows for top five SKUs and overall totals. Constraints: only include SKUs with at least [min_units] sold in the quarter and exclude SKUs with negative revenue.
2. Inventory turnover drill: Act as an inventory optimization consultant. Task: compute inventory turnover by [warehouse] for the last [period]. Context: Google-like data in an Excel sheet with SKU, on_hand, cost_per_unit, sales_per_month. Output: a dashboard-ready table showing turnover_ratio, days_on_hand, and recommended reorder_points for top 10 SKUs. Format: bullet list per SKU. Constraints: use rolling averages and flag SKU with excessive stock.
3. Pricing impact analysis: Role: pricing analyst. Task: determine the impact of a price change on revenue and margin using an Excel dataset with columns SKU, price_before, price_after, revenue_before, revenue_after, costs. Context: include at least two competing price points. Output: a comparison table and a short interpretation stating whether the price change increased gross margin and revenue. Format: concise paragraph and table. Constraints: calculate elasticity per SKU and aggregate at category level.
4. A/B test revenue lift: You are an experiments analyst. Task: compute revenue lift from an A/B test using an Excel file with fields group, sku, date, revenue. Context: include sample_size, p_value, confidence. Output: a result summary with lift percentage, statistical significance, and recommended actions. Format: JSON-like table for easy parsing. Constraints: ignore non-test days and provide caveated notes.
5. Review sentiment map: Role: customer insights analyst. Task: map review sentiment by product using an Excel sheet with product_id, review_text, rating, date. Context: provide a sentiment_score per review and aggregate by product. Output: a heatmap-ready dataset with product_id, average_sentiment, neutral_count, negative_count. Format: CSV-ready table. Constraints: exclude reviews after a given cutoff date.
6. Profitability by category: You are a finance analyst. Task: classify each SKU into profitability buckets using cost, fees, and revenue in an Excel sheet. Context: columns: SKU, category, cost_of_goods, shipping, fees, revenue. Output: summary by category with average margin, total profit, and recommended price ranges. Format: table with bullets for categories. Constraints: treat SKUs with low volume separately.
7. Forecast by seasonality: Act as a forecasting specialist. Task: build a seasonality-adjusted forecast for next [n_months] using historical monthly revenue data in Excel. Context: columns include month, revenue, promo_spend, ad_spend. Output: forecast table with monthly projections and confidence bands. Format: two tables: base forecast and scenario variants. Constraints: show upper and lower bounds.
8. Inventory reorder plan: Role: operations planner. Task: generate a reorder plan for the next [period] based on on_hand, lead_time, and demand_forecast in an Excel sheet. Context: data by SKU and warehouse. Output: reorder points, order quantities, and target stock levels. Format: CSV-ready rows for top 20 SKUs. Constraints: consider service level target.
9. Sell-through rate tracker: You are a merchandising analyst. Task: calculate sell-through rate by SKU over a given period in an Excel workbook with columns sku, sold, received, date. Output: a tracker table with sku, sell_through, days_on_hand, and suggested promotions. Format: compact table. Constraints: filter out discontinued items.
10. Advertising ROI map: Role: advertising analytics specialist. Task: compute ROI by product and campaign from an Excel dataset with columns campaign_id, product_id, ad_spend, revenue_attributed, clicks, impressions. Output: a matrix showing ROI per product per campaign and a recommended optimization plan. Format: matrix plus bullet recommendations. Constraints: adjust for attribution window.
11. Cost to acquire per SKU: Role: finance analyst. Task: compute CAC per SKU using ad_spend and new_customer_revenue in an Excel file. Context: Include seasonality flags. Output: per-SKU CAC with benchmarking against average CAC and profitability threshold. Format: table. Constraints: exclude SKUs with zero new customers.
12. Fulfillment cost drilldown: You are an operations cost analyst. Task: break down fulfillment costs by fulfillment_center, carrier, and product in an Excel sheet. Context: fields: sku, center, carrier, cost, units. Output: cost breakdown table and recommended efficiency actions. Format: bullet list per center. Constraints: normalize costs to unit cost.
13. Top products by margin: Role: business analyst. Task: identify top five margin-bearing SKUs from Excel data with columns sku, revenue, cost_of_goods, other_costs. Output: ranking table and a quick narrative explanation. Format: table and short paragraph. Constraints: exclude items with negative margin.
14. Channel mix by revenue: You are a channel analytics expert. Task: compute revenue by sales channel from an Excel dataset with fields channel, sku, revenue, units. Output: a channel mix report with shareByChannel, top channels, and recommended focus. Format: table and bullet tips. Constraints: adjust for returns.
15. Return rate by SKU: Role: operational analyst. Task: calculate return rate by SKU over period using columns sku, returns, units_sold. Context: include warranty data as optional. Output: a chart-ready table with sku, return_rate, and recommended actions. Format: table. Constraints: exclude samples with high non-returnability.
16. Pricing sensitivity check: You are a pricing strategist. Task: run a sensitivity check for price bands using an Excel dataset of price_band, revenue, units_sold, margin. Output: table of elasticities by band and recommended price guardrails. Format: table with notes. Constraints: treat outliers carefully.
17. Stockout risk alert: Role: inventory risk analyst. Task: flag stockout risk per SKU using on_hand, forecast_demand, lead_time in Excel. Output: a risk score and recommended safety stock. Format: list per SKU. Constraints: thresholds by category.
18. Promotion impact delta: You are a promotions analyst. Task: measure delta in revenue and profit from promotions in an Excel sheet with promo_id, sku, revenue, profit, promo_cost. Output: impact table and recommended next steps. Format: table and short note. Constraints: isolate promo effects.
19. Competitor price parity: Role: competitive intelligence analyst. Task: compare your prices to competitors using an Excel sheet of sku, your_price, competitor_price. Context: include price_delta. Output: list of items with price_delta and recommended actions. Format: bullet list. Constraints: ensure data quality.
20. Currency and fees exposure: You are a financial risk analyst. Task: assess currency and marketplace fee exposure in Excel, by region and currency, onto revenue and margin. Output: exposure map and mitigation actions. Format: table and bullet list. Constraints: normalize for exchange rate fluctuations.
21. Customer lifetime value: Role: customer analytics lead. Task: compute LTV by cohort using an Excel dataset with customer_id, purchase_value, date, cohort. Output: cohort-based LTV table and action recommendations. Format: table and short narrative. Constraints: include censoring for churn.
22. Refund rate forecast: You are a customer service data analyst. Task: forecast refund rate by product using historical refund_rate, revenue, and returns in Excel. Output: forecast table and confidence interval. Format: table with notes. Constraints: separate fulfilled vs. unfulfilled refunds.
23. Seasonal demand map: Role: demand planner. Task: create a seasonal demand map by month and product in Excel using historical sales. Output: heatmap-ready table with month, product, demand_index. Constraints: align with marketing calendar.
24. Stock aging report: You are a warehouse data analyst. Task: generate stock aging report by SKU and days_in_stock in Excel. Output: aging buckets, risk flags, and recommended actions. Format: table. Constraints: include inbound and outbound FIFO notes.
25. Fulfilled vs unfulfilled: Role: logistics analyst. Task: analyze fulfillment performance by SKU and fulfillment_center in Excel. Output: table showing on_time_delivery, delay_rate, and improvement plan. Format: table with bullets. Constraints: consider backorder impact.
26. Ad spend efficiency: You are an media analytics specialist. Task: compute ad_spend efficiency by SKU in Excel using fields ad_spend, revenue, CAC, ROAS. Output: KPI sheet and recommended optimizations. Format: table with notes. Constraints: exclude non-click attribution.
27. Worst performing SKUs: Role: portfolio manager. Task: identify bottom five SKUs by contribution margin in Excel. Output: list with margin, revenue, and suggested actions. Format: table and bullets. Constraints: exclude discontinued items.
28. Review sentiment trends: You are a customer insights analyst. Task: track review sentiment over time by product in Excel. Output: trend chart data and interpretation for management. Format: table and short summary. Constraints: separate verified reviews.
29. Discount strategy matrix: Role: pricing and promotions expert. Task: build a discount strategy matrix in Excel with discount_band, expected_profit, expected_sales. Output: recommended discount bands and guardrails. Format: two tables. Constraints: test cross-elasticities.
30. Inventory aging insights: You are an operations analyst. Task: analyze aging inventory and propose actions in Excel with fields sku, days_in_stock, on_hand, category. Output: aging report with alert flags and actions. Format: table and bullets. Constraints: flag slow movers.
31. SKU rationalization: Role: product analyst. Task: recommend SKU cuts using profitability and growth potential in Excel. Output: list of SKUs to discontinue with rationale and success metrics. Format: table and short narrative. Constraints: preserve inventory replenishment.
32. Forecast accuracy audit: You are a data quality engineer. Task: audit forecast accuracy by SKU comparing forecast vs actuals in Excel. Output: accuracy metrics, dashboard snippet, and remediation steps. Format: table and bullets. Constraints: compute MAPE and tracking signal.
33. Promotions vs baseline: Role: promotions analyst. Task: compare revenue during promotions to baseline in Excel. Output: lift table by promotion type and SKU with confidence intervals. Format: table and interpretation. Constraints: separate carryover effects.
34. Category growth heatmap: You are a category analyst. Task: build a growth heatmap by category and quarter in Excel. Output: heatmap data with growth_rate and rank. Constraints: normalize per category.
35. Shipping cost pressure: Role: logistics finance analyst. Task: analyze shipping cost pressure by region and product in Excel. Output: cost pressure report with suggested optimizations. Format: table and bullet recommendations. Constraints: factor dim weight.
36. Supplier cost delta: You are a procurement analyst. Task: calculate supplier cost delta over time in Excel. Output: delta table by supplier and SKU with recommended actions. Format: table. Constraints: account for volume discounts.
37. Return reasons drilldown: Role: customer care data analyst. Task: categorize return reasons by SKU in Excel. Output: reason map with top causes and action plan. Format: table with notes. Constraints: link to product issues.
38. Top promo performers: You are a promotions analyst. Task: identify top performing promotions by incremental revenue in Excel. Output: table with promo_id, uplift, ROI, SKU mix. Format: table and short narrative. Constraints: limit to active promos.
39. Net profit per order: Role: finance analyst. Task: compute net profit per order from order-level data in Excel. Output: per-order profit, average, and distribution. Format: table and histogram notes. Constraints: exclude refunds.
40. Discount erosion risk: You are a pricing risk analyst. Task: assess discount erosion risk across channels in Excel. Output: erosion score by channel and SKU with mitigation steps. Format: table and bullets. Constraints: consider tiered pricing.
41. Inventory safety stock: Role: supply planner. Task: determine safety stock per SKU using service level, demand variability, and lead time in Excel. Output: safety stock recommendations and reorder points. Format: table. Constraints: separate fast-moving from slow-moving items.
42. SKU performance dashboard: You are a data visualization lead. Task: build a compact SKU performance dashboard dataset in Excel with core metrics. Output: list of key metrics per SKU and a summary. Format: table and bullet notes. Constraints: keep under 12 columns.
43. Bulk pricing simulation: Role: pricing analyst. Task: simulate bulk pricing scenarios in Excel with volume tiers, unit_cost, and demand. Output: table of revenue, profit, and break-even units for each tier. Format: table. Constraints: exclude unrealistic volumes.
44. Profit by region: You are a regional finance analyst. Task: calculate profit by region from revenue, cost, and fees in Excel. Output: regional profit map and top regions. Format: table and map notes. Constraints: currency normalization.
45. AOV trends: Role: ecommerce analyst. Task: analyze average order value trends by product category in Excel. Output: trend data and a short guidance for pricing or bundling. Format: table and notes. Constraints: adjust for promotions.
46. Refund delay analysis: You are a customer service data scientist. Task: analyze refund delays by reason and product in Excel. Output: delay histogram and action plan. Format: table and bullets. Constraints: differentiate refunds approved vs denied.
47. Inventory by age bucket: Role: inventory analyst. Task: assign age buckets to inventory in Excel and report outliers. Output: age bucket table with stock value and recommended actions. Format: table. Constraints: define bucket ranges.
48. Channel profitability map: You are a channel finance lead. Task: map profitability by channel and SKU in Excel. Output: channel profitability matrix and optimization notes. Format: matrix and bullets. Constraints: consider returns and fees.
49. Promo cadence plan: Role: promotions planner. Task: design a promo cadence in Excel aligned to holidays and inventory. Output: calendar with promo_id and expected impact. Format: table. Constraints: avoid overlap.
50. SKU risk profiling: You are a risk analyst. Task: profile SKUs by risk of obsolescence using sales velocity, seasonality, and supplier risk. Output: risk score and mitigation actions. Format: table and bullets. Constraints: weight factors by category.
51. Quality issue correlation: Role: product quality analyst. Task: correlate defect reports with returns by SKU in Excel. Output: correlation table and recommended QA actions. Format: table. Constraints: require at least five samples.
52. Bundle optimization: You are a merchandising analyst. Task: test bundle combinations in Excel with SKU, companion_sku, price, and demand. Output: bundle profitability table and recommended bundles. Format: table. Constraints: avoid cannibalization.
53. Customer segment map: Role: customer analytics lead. Task: map segments by purchase behavior in Excel. Output: segment definitions, revenue share, and target actions. Format: table and bullet list. Constraints: ensure mutual exclusivity.
54. PO accrual forecast: You are a finance analyst. Task: forecast purchase order accruals from supplier data in Excel. Output: accrual forecast by month and supplier. Format: table. Constraints: seasonality adjustments.
55. Sales channel delta: Role: sales operations analyst. Task: compare channel performance over time in Excel. Output: delta table by channel, SKU, and revenue. Format: table with notes. Constraints: normalize for seasonality.
56. Inventory loss risk: You are an internal controls auditor. Task: estimate inventory loss risk using shrinkage, theft incidents, and variance in Excel. Output: risk score and controls plan. Format: table and bullets. Constraints: require evidence trail.
57. Launch readiness check: Role: product operations lead. Task: assess readiness of new SKU launch data in Excel. Output: launch readiness score and action list. Format: table and bullets. Constraints: align with supply constraints.
58. Top performing bundles: You are a merchandising analyst. Task: identify bundles with highest margin and cross-sell potential in Excel. Output: bundle_id, margin, uplift, SKU mix. Format: table and brief narrative. Constraints: limit to active bundles.
59. Seasonal stock plan: Role: inventory planner. Task: plan seasonal stock by product family in Excel. Output: stock vector by month and family with recommendations. Format: table and bullets. Constraints: avoid overstock.
60. NAI by SKU: You are a data analyst. Task: calculate net revenue by SKU after returns and refunds in Excel. Output: net revenue table and margin check. Format: table. Constraints: exclude test SKUs.
61. Price floor check: Role: pricing auditor. Task: verify price floors and minimum advertised prices in Excel. Output: price floor compliance report and exceptions. Format: table. Constraints: reference policy.
62. Demand shaping plan: You are a demand planner. Task: create a demand shaping plan using promotions, pricing, and inventory in Excel. Output: plan with expected lift, required inventory, timeline. Format: table and bullets. Constraints: tiered tactics by channel.
63. SKU health score: Role: product analytics lead. Task: compute health scores for SKUs from multiple metrics in Excel. Output: score, ranking, and remediation steps. Format: table and notes. Constraints: normalize to 0-100.
64. Replenishment optimization: You are a supply chain analyst. Task: optimize replenishment cycles in Excel by SKU and warehouse. Output: recommended cycle length and quantity. Format: table. Constraints: align with lead times.
65. Discount leakage audit: Role: promotions auditor. Task: audit discount leakage across channels in Excel. Output: leakage report with hotspots and controls. Format: table and bullets. Constraints: exclude stackable discounts.
66. Forecast bias investigation: You are a data scientist. Task: investigate forecast bias by SKU in Excel. Output: bias summary and remediation steps. Format: table and text notes. Constraints: separate bias by season.
67. Product launch KPIs: Role: product ops lead. Task: track launch KPIs for new SKUs in Excel. Output: KPI dashboard rows for core metrics and a narrative. Format: table and bullets. Constraints: compare against baseline.
68. Cost per sale analysis: You are a financial analyst. Task: compute cost per sale by channel in Excel. Output: CPAs by channel with recommended adjustments. Format: table and notes. Constraints: exclude organic channels.
69. Customer churn risk: Role: customer insights analyst. Task: flag high churn risk by cohort in Excel. Output: churn risk table and retention actions. Format: table and bullets. Constraints: tier by revenue.
70. Promotions overlap check: You are a promotions planner. Task: identify overlapping promotions in Excel and quantify cannibalization. Output: overlap table and mitigation steps. Format: table and notes. Constraints: no more than two overlapping promos per SKU.
71. SKU profit map: Role: business intelligence analyst. Task: map profit by SKU and subcategory in Excel. Output: profit heatmap and conclusions. Format: table and short narrative. Constraints: reflect non-linear costs.
72. Review risk index: You are a risk analyst. Task: create a review risk index by product in Excel. Output: risk score, trigger events, and mitigation actions. Format: table and bullets. Constraints: update quarterly.
73. Inventory turnover by region: Role: regional operations analyst. Task: compute inventory turnover by region using Excel data. Output: region table and recommendations. Format: table and bullets. Constraints: adjust for regional pricing differences.
74. Gross margin guardrails: You are a margin specialist. Task: set guardrails by product category in Excel. Output: margin targets and alert rules. Format: table and notes. Constraints: update monthly.
75. SKU health checklist: Role: product operations manager. Task: run a health checklist for SKUs in Excel. Output: checklist results and remediation list. Format: table and bullets. Constraints: include data completeness.
76. Pricing band optimization: You are a pricing strategist. Task: optimize pricing bands in Excel using elasticities and costs. Output: recommended bands and ranges. Format: table and notes. Constraints: avoid overfitting.
77. Voice of customer map: Role: customer insights lead. Task: map VOC topics by product in Excel. Output: topic heatmap and action plan. Format: table and bullets. Constraints: derive from reviews.
78. SKU ending life: You are a product lifecycle analyst. Task: identify SKUs nearing end of life in Excel using sales decay and inventory. Output: list with rationale and sunset plan. Format: table and notes. Constraints: ensure smooth wind-down.
79. RFM segmentation: Role: marketing data analyst. Task: segment customers by recency, frequency, monetary using Excel. Output: RFM segments with revenue and actions. Format: table and bullets. Constraints: ensure sufficient sample size.
80. Product bundle viability: You are a merchandising analyst. Task: test new bundle ideas in Excel with cross-sell potential and margins. Output: viability score and recommended bundles. Format: table and narrative. Constraints: avoid diluting core SKUs.
81. Demand by customer segment: Role: demand planner. Task: forecast demand by customer segment in Excel. Output: segment-level projections and risks. Format: table and notes. Constraints: separate high-value segments.
82. Inventory forecast baseline: You are a data analyst. Task: build a baseline inventory forecast in Excel from historical data. Output: monthly forecast by SKU and safety stock. Format: table and bullets. Constraints: include promo effects.
83. SKU lifecycle chart: Role: product analyst. Task: chart SKU lifecycle stage distribution in Excel. Output: stage counts and recommendations. Format: table and narrative. Constraints: define stages clearly.
84. Hyperlocal demand check: You are a market analyst. Task: evaluate demand by local region in Excel. Output: regional demand table and localization plan. Format: table and bullets. Constraints: adjust pricing for region.
85. Vendor payment timing: Role: finance ops. Task: analyze payment terms and discounts in Excel. Output: payment timing optimization and cashflow impact. Format: table and notes. Constraints: align with PO approvals.
86. Bundle revenue stability: You are a merchandising analyst. Task: assess bundle revenue stability using historical data in Excel. Output: volatility metrics and recommendations. Format: table and bullets. Constraints: segment bundles by family.
87. Returns by reason trend: Role: returns analyst. Task: trend returns by reason and SKU in Excel. Output: trend lines and recommended QA fixes. Format: table and notes. Constraints: require at least ten samples.
88. Profit by listing type: You are a product analyst. Task: compare profit by listing type (FBA, MFN, FBM) in Excel. Output: table with margins and recommendations. Format: table and short narrative. Constraints: normalize for fees.
89. Ad creative performance: Role: creative analytics lead. Task: analyze ad creative performance by variant in Excel. Output: performance table and best-performing variants. Format: table and bullets. Constraints: require sufficient impressions.
90. Risk-adjusted revenue: You are a finance analyst. Task: compute risk-adjusted revenue for SKUs in Excel based on volatility. Output: table with risk score and adjusted revenue. Format: table and notes. Constraints: define risk levels.
91. Lifecycle stage actions: Role: product ops. Task: list actions per SKU by lifecycle stage using Excel. Output: stage-to-action mapping and owners. Format: table and bullets. Constraints: align with inventory.
92. Ad budget pacing: You are a media planner. Task: paceAd budget across weeks in Excel with spend and revenue. Output: pacing plan and alerts. Format: table and notes. Constraints: avoid overspend.
93. SKU correlations: Role: data scientist. Task: compute correlations between SKUs sales using Excel data. Output: correlation matrix and interpretation. Format: table and notes. Constraints: handle multicollinearity.
94. Seasonal promo calendar: You are Promotions planner. Task: build a seasonal promo calendar in Excel with promotions and impact estimates. Output: calendar with risk flags. Format: table and bullets. Constraints: ensure non-overlapping promotions.
95. Forecast reconciliation: Role: data manager. Task: reconcile forecast with plan in Excel. Output: reconciliation table and variance analysis. Format: table and notes. Constraints: highlight material variances.
96. SKU clustering: You are a data analyst. Task: cluster SKUs by profitability and turnover in Excel. Output: cluster definitions and recommended actions. Format: table and narrative. Constraints: choose meaningful distance metric.
97. Promo impact by channel: Role: promotions analyst. Task: attribute promotion impact by channel in Excel. Output: channel-lift table and action plan. Format: table and bullets. Constraints: control for seasonality.
98. Cost perClick comparison: You are an advertising analyst. Task: compare CPC efficiency by channel in Excel. Output: CPC table with recommendations. Format: table and notes. Constraints: separate branded vs generic.
99. SKU risk mitigation: Role: risk manager. Task: craft mitigation steps for high-risk SKUs in Excel. Output: risk reducer table and trigger points. Format: table and bullets. Constraints: escalate when thresholds hit.
100. Executive summary draft: You are a senior analytics lead. Task: draft a one-page executive summary of the Excel data analysis for Amazon sellers in Excel-ready format. Output: concise summary with highlights, risks, and actions. Format: table and bullets. Constraints: keep to one page.

Best Practices

• Keep prompts domain-specific: always anchor to Amazon seller metrics such as revenue, margin, and sell-through. Use explicit constraints and output formats to improve consistency. Reuse prompts as templates for similar SKUs or categories. Periodically review prompts to reflect policy or marketplace changes. Combine prompts with a simple data quality check before feeding Claude.

Common Mistakes to Avoid

• Using placeholders without defining their values leads to ambiguous outputs.
• Asking for too broad analyses in one prompt causes noisy results.
• Ignoring data quality issues like missing values or incorrect dates undermines insights.
• Not specifying output format or constraints makes integration with dashboards harder.
• Forgetting to specify currency, time zone, and seasonality can skew results.

FAQ

What data do I need in Excel to use these prompts effectively?

You need a clean, query-ready Excel workbook with at least SKU, date, revenue, units_sold, costs, and channel fields, plus optional fields like returns, ad_spend, and lead_time.

Can I run these prompts without programming skills?

Yes. The prompts are designed to be copy-pasteable into Claude and produce structured outputs that can be copied into dashboards or CSV files.

How do I verify Claude outputs quickly?

Cross-check a sample of results against the source Excel data and spot-check calculations like margins and ROI for accuracy.

Do these prompts adapt to different Amazon marketplaces?

Yes. You can include a region or marketplace field and segment outputs by marketplace to compare performance.

Can I automate these prompts for regular reports?

Yes. Save prompts as templates and schedule Claude usage to update dashboards weekly or monthly.

Are there prompts focused on inventory and refunds separately?

Yes. Several prompts focus on inventory dynamics, stock age, and refunds, with separate outputs and notes.