100 Best Claude KPI and Dashboard Prompts for Shopify Stores

Direct Answer

The best Claude prompts for KPI and dashboard for Shopify stores are provided below as a prepared library. Use them to generate precise metrics, dashboards, and actionable insights for Shopify performance. Each prompt includes role, task, context, output format, and constraints to ensure usable outputs.

Introduction

This page is a practical prompt library for KPI and dashboard optimization tailored to Shopify stores. It helps store owners, analysts, and marketers get fast, exact insights without building analytics from scratch.

The prompts are designed to be copyable and ready to run in Claude, so you can deploy dashboards and KPI reports with minimal setup and maximum clarity.

How to Use These Claude Prompts

• Replace placeholders like [industry], [audience], [product], [constraints], [data], [tone], and [format] with your specifics.
• Add constraints to tighten scope, such as date ranges or metric definitions.
• Request outputs in a consistent format (tables, bullet lists, or short paragraphs) to simplify integration into dashboards.
• Verify outputs by cross-checking with your Shopify reports and, if needed, rerun with adjusted parameters.
• When reusing prompts across weeks, save the exact output format and constraints to maintain consistency.
• Use prompts to generate both high-level summaries and drill-down analyses for quick executive views and detailed operational insights.

100 Best Claude KPI and Dashboard Prompts for Shopify Stores

1. Sales overview prompt: You are a data analyst for a Shopify store. Task: Generate a daily sales summary for the previous day including total revenue, average order value, orders count, and top five products by revenue. Context: Use data from the Shopify store, compare to the prior day, and output in a concise table with columns: metric, value, delta vs prior day, and trend arrow. Output format: plain text table with two decimal places for currency. Constraints: focus on Shopify revenue channels, exclude refunds unless they exceed 2 percent of revenue.
2. Revenue by channel: You are a Shopify analytics specialist. Task: Produce a breakdown of revenue by sales channel (online store, point of sale, marketplace integrations) for the current month. Context: Include channel contributions, growth vs last month, and top performing products per channel. Output format: bullet list with channel name, revenue, month-over-month growth, and top product. Constraints: exclude returns; show percentages up to one decimal.
3. Conversion funnel steps: You are a Shopify funnel analyst. Task: Map the checkout funnel from visit to purchase, providing conversion rates at each step (site visit to product page, add to cart, initiate checkout, purchase). Context: Use last 14 days data from Shopify. Output format: ordered list with step, action, conversion rate, and recommended optimization. Constraints: diagnose at least two actionable improvements per step.
4. Cart abandonment insights: You are a Shopify UX researcher. Task: Identify reasons for cart abandonment using logs and events from Shopify plus customer interviews. Context: Provide top three abandonment drivers with quantified impact, and offer fixes with expected lift. Output format: numbered recommendations with impact estimates. Constraints: each fix must include a quick risk assessment.
5. AOV trend analysis: You are a data analyst. Task: Analyze average order value over the last 12 weeks, identify seasonal patterns, and compute a 4-week forecast. Context: Compare to same period last year and provide confidence interval. Output format: small chart-free forecast table with week, actual AOV, forecast AOV, and 95 percent interval.
6. Top products dashboard: You are a product performance analyst. Task: Create a dashboard snippet listing top 10 products by revenue this month, with price, units sold, revenue, and margin proxy. Context: Include color-coded profitability indicator and a link to product page. Output format: plain text table suitable for a dashboard card.
7. Customer lifetime value: You are a CRM analytics specialist. Task: Estimate customer lifetime value (CLV) for the current cohort of new customers acquired in the last 90 days, using a simple LTV model. Context: Include cohort size, average value per customer, and expected retention. Output format: concise summary with a one-line table of metrics.
8. Churn risk indicators: You are a Shopify analytics agent. Task: Identify Shopify customers at risk of churn in the next 30 days using order history, engagement events, and support interactions. Context: Provide top risk factors and recommended retention actions. Output format: bullet list with risk factor, data signal, suggested action.
9. Marketing channel ROI: You are a marketing analytics specialist. Task: Compute ROI for major marketing channels this month (Facebook, Google, email, affiliates). Context: Include ad spend, revenue, attributed orders, and ROAS. Output format: summary table and a one-sentence takeaway.
10. Traffic sources breakdown: You are a data analyst. Task: Break down traffic sources by sessions, new users, and conversion rate for the current week. Context: Include organic search, paid search, social, referral, email. Output format: columnar list with source, sessions, conversions, conversion rate.
11. Refund impact report: You are a Shopify analytics expert. Task: Quantify impact of refunds on revenue this month, including refund rate, net revenue, and top refunded products. Context: Compare to last month and provide remediation steps. Output format: compact report with bullet points.
12. Inventory sell-through: You are an operations analyst. Task: Calculate sell-through rate by SKU for the past 30 days and identify slow movers. Context: Provide suggested price or promotion adjustments per SKU. Output format: table with SKU, sell-through, recommendation.
13. Promotions impact: You are a promotions analyst. Task: Evaluate the impact of current promotions on units sold and revenue, including lift vs baseline. Context: Use last promotion period as baseline and output a summary with recommended next offer. Output format: two-column comparison table.
14. Cohort retention: You are a data scientist for Shopify. Task: Build a simple 7-day retention cohort analysis for new customers acquired this month. Context: Show retention by cohort and provide actionable insights. Output format: small chart-free table and one-sentence takeaway.
15. Cart value by device: You are a data analyst. Task: Compare cart values by device type (desktop, mobile, tablet) over the last 14 days. Context: Highlight mobile optimization opportunities. Output format: table with device, average cart value, standard deviation.
16. Checkout speed: You are a UX analytics specialist. Task: Measure checkout page load times and their correlation with conversion rate for the last 7 days. Context: Provide three optimization ideas with expected impact. Output format: bullet list.
17. Product performance heatmap: You are a product analytics expert. Task: Create a heatmap-style ranking of products by revenue per category in the current month. Context: Include category, product, revenue, and rank. Output format: text table.
18. Customer segmentation: You are a marketing data scientist. Task: Segment customers into three groups based on recency, frequency, and monetary value (RFM) using last 12 months data. Context: Provide each segment size and a typical action per segment. Output format: bullet list with segment name and actions.
19. Active customers trend: You are a Shopify analytics agent. Task: Track daily active customers for the last 30 days and highlight spikes or dips with possible causes. Output format: daily values and a short interpretation.
20. Repeat purchase rate: You are an analytics specialist. Task: Calculate repeat purchase rate for customers acquired in the last six months and compare to overall rate. Output format: two-number summary and a recommendation.
21. Pricing sensitivity: You are a pricing analyst. Task: Analyze how small price changes affect demand for best-selling SKUs over the last eight weeks. Context: Provide a simple elasticity estimate and recommendation. Output format: table with price, demand, and elasticity.
22. Support load forecast: You are a operations planner. Task: Forecast customer support ticket volume for the next 14 days based on historical data and promotions. Output format: forecast with date, expected tickets, and confidence level.
23. Fulfillment latency: You are a logistics analyst. Task: Calculate average fulfillment latency by warehouse and product category for the last two weeks. Context: Highlight delays and workaround steps. Output format: table with warehouse, category, latency days.
24. Refund per order: You are a financial analyst. Task: Compute average refund per order and identify top refunded products by revenue. Context: Provide mitigation actions. Output format: concise table.
25. Top customer cohorts: You are a data analyst. Task: Identify top customer cohorts by revenue in the last 90 days and outline retention actions per cohort. Output format: bullet list with cohort label and actions.
26. Email campaign lift: You are an email marketing analyst. Task: Measure lift from last email campaign on revenue, CTR, and orders. Context: Provide attribution notes and optimization tips. Output format: summary with key metrics.
27. Checkout abandon reasons: You are a UX researcher. Task: List top reasons customers abandon at checkout with supporting signals from events and forms. Output format: numbered list of reasons and fixes.
28. Average days to first order: You are a customer analytics specialist. Task: Compute average days from first site visit to first order for new customers this quarter. Output format: one-line metric and two supporting notes.
29. Top landing pages: You are a web analytics agent. Task: Identify top landing pages by conversion rate this week and suggest optimization actions. Output format: table with page, sessions, conversions, conversion rate.
30. Refund cause map: You are a financial data scientist. Task: Map common refund causes to product categories and propose remediation. Output format: bullet list with cause and recommended action.
31. Inventory forecast: You are a supply chain planner. Task: Forecast 8 weeks of SKU-level demand using last 8 weeks data and seasonality. Output format: table with SKU, forecast units, confidence.
32. Fulfillment cost view: You are a logistics analyst. Task: Break down fulfillment costs by carrier, warehouse, and order size for the current month. Output format: table with cost center, amount.
33. Cart conversion by source: You are a data analyst. Task: Compare cart conversion rate by traffic source for the last 7 days. Context: Provide source-specific optimization ideas. Output format: table.
34. Lifetime value by segment: You are a CRM analyst. Task: Compute CLV by customer segment defined by engagement level in last 12 months. Output format: table with segment, CLV, confidence.
35. Product margin snapshot: You are a finance analyst. Task: Provide margin proxy for top 20 SKUs this month and flag any margin outliers. Output format: simple table.
36. Sales by region: You are a regional sales analyst. Task: Generate sales by region for this month with growth vs last month and top regions by revenue. Output format: table.
37. Order processing time: You are a operations analyst. Task: Measure order processing time from order received to fulfilled for the last 10 days. Output format: table with order count and average days.
38. Promo performance map: You are a promotions strategist. Task: Map promotions to revenue lift by program and product group, with takeaway recommendations. Output format: bullet list.
39. Cart size by channel: You are a data analyst. Task: Compare average cart size across online store and mobile app this week. Output format: table.
40. Retention by cohort: You are a data scientist. Task: Show 30-day retention by customer cohort defined by signup week, with actionable insights per cohort. Output format: table and one-line takeaway.
41. Shipping speed targets: You are a logistics planner. Task: Propose shipping speed targets by region and product type based on historical delivery times. Output format: bullet list with targets and rationale.
42. Product recommendation signals: You are a merchandising analyst. Task: Identify signals driving product recommendations for customers in the last 14 days. Output format: bullet list with signal and suggested action.
43. Creative assets impact: You are a marketing data analyst. Task: Correlate creative assets used in ads with incremental revenue and CTR in the last campaign. Output format: short findings table.
44. Free vs paid channels: You are a marketing analyst. Task: Compare performance of free vs paid channels this month across revenue, orders, and CAC. Output format: table with metrics.
45. Repeat visit rate: You are a web analytics expert. Task: Calculate repeat visit rate for shoppers over the last 60 days and propose re-engagement tactics. Output format: two-number summary and plan.
46. Checkout form optimization: You are a UX researcher. Task: List top form fields causing friction in checkout and provide redesign suggestions. Output format: bullet list with field name and fix.
47. Product return reasons: You are a product analyst. Task: Compile top reasons for product returns by category and propose quality or procurement actions. Output format: bullet list.
48. Checkout energy optimization: You are a performance engineer. Task: Analyze page load components affecting checkout conversion and suggest optimizations with expected impact. Output format: short list.
49. Seasonal demand forecast: You are a demand planner. Task: Forecast seasonal demand for the next 8 weeks for core SKUs using last year patterns. Output format: table with week and forecast.
50. Loyalty program impact: You are a loyalty analytics expert. Task: Measure impact of loyalty program on repeat purchases and average order value. Output format: summary with figures.
51. Cart recovery prompts: You are a conversion specialist. Task: Propose cart recovery prompts and timing for abandoned carts with expected lift. Output format: bullet list.
52. Product page engagement: You are a site analytics agent. Task: Analyze engagement signals on product pages (time on page, scroll depth) and link to potential improvements. Output format: table.
53. Mobile checkout improvements: You are a mobile UX specialist. Task: Suggest two mobile checkout optimizations to reduce friction and increase conversions. Output format: bullet list.
54. Tax and shipping summary: You are a finance analyst. Task: Generate a tax and shipping revenue summary for the current month by region. Output format: table.
55. Abandoned checkout signals: You are a data analyst. Task: Identify signals preceding checkout abandonment in the last 7 days and rank by predictive power. Output format: numbered list with signal and rationale.
56. Forecast accuracy check: You are a data engineer. Task: Compare forecast vs actual for sales this month and suggest model improvements. Output format: short paragraph and a table of errors.
57. Inventory overflow risk: You are an operations analyst. Task: Detect SKU overstock risk this quarter and propose clearance actions. Output format: bullets with SKU and action.
58. Cross-sell recommendations: You are a merchandising specialist. Task: Suggest top three cross-sell recommendations for the next 7 days based on purchase patterns. Output format: bullet list.
59. Order value by time: You are a data analyst. Task: Break down average order value by hour of day for the current week. Output format: table with hour and AOV.
60. Refund trend by product: You are a financial analyst. Task: Analyze refunds by product and provide mitigation steps for top refunded items. Output format: table and notes.
61. New customer onboarding: You are a CRM strategist. Task: Outline a 4-step onboarding sequence for new Shopify customers to boost activation and repeat purchases. Output format: bullet list.
62. Abandoned cart timing: You are a conversion specialist. Task: Determine optimal timing for cart abandonment emails/messages based on shopper behavior in the last 10 days. Output format: short list.
63. Shipping cost optimization: You are a logistics analyst. Task: Propose shipping cost optimizations based on carrier rates and order size in the current month. Output format: bullet list.
64. Quality issue detection: You are a quality assurance analyst. Task: Flag products with high return rates and correlate with supplier or batch data. Output format: table.
65. Cohort revenue analysis: You are a data scientist. Task: Compare revenue by signup week cohorts over the last 120 days and identify high-value cohorts. Output format: table and interpretation.
66. Checkout A/B test ideas: You are a experimentation specialist. Task: Propose five A/B test ideas for checkout flow with expected outcomes and success criteria. Output format: bullet list.
67. Promotions conflict check: You are a pricing analyst. Task: Detect conflicting promotions running simultaneously and propose consolidation to avoid cannibalization. Output format: short bullets.
68. SKU profitability by region: You are a finance analyst. Task: Compare SKU-level profitability by region and highlight red zones. Output format: table with SKU, region, margin.
69. Cart frequency analysis: You are a data scientist. Task: Compute cart frequency for returning customers over the last 60 days and suggest loyalty actions. Output format: table and plan.
70. New product viability: You are a product analyst. Task: Assess viability of adding a new product line based on last 6 months demand signals. Output format: concise report with go/no-go criteria.
71. AOV by channel: You are a data analyst. Task: Break down average order value by channel (online store, app, marketplaces) for the current month. Output format: table.
72. Inventory aging report: You are an inventory analyst. Task: Show aging of inventory by SKU and propose slow-move clearance actions. Output format: table.
73. Refund policy impact: You are a finance analyst. Task: Evaluate how the current refund policy affects customer satisfaction and revenue risk. Output format: brief findings with recommendations.
74. Cross-channel attribution: You are a marketing data scientist. Task: Provide attribution shares for marketing touchpoints across channels for the current month. Output format: bullet list.
75. Top influencers impact: You are a marketing analyst. Task: Identify top affiliate or influencer sources driving sales this week and suggest optimization actions. Output format: table.
76. Support sentiment drift: You are a customer insights analyst. Task: Analyze support ticket sentiment over the last 30 days and flag negative trends with remedies. Output format: short report.
77. Checkout retry strategy: You are a conversion strategist. Task: Propose a 3-step checkout retry strategy for failed payments with timing and messaging. Output format: bullet list.
78. LTV by channel: You are a CRM analytics expert. Task: Estimate customer lifetime value by acquisition channel and provide actionable notes per channel. Output format: table.
79. Product page speed: You are a performance analyst. Task: Measure product page load times and correlate with bounce rate; recommend optimizations. Output format: bullet list.
80. Seasonal promo calendar: You are a promotions planner. Task: Create a 8-week seasonal promo calendar with themes, target SKUs, and expected outcomes. Output format: bullet list.
81. Cart recovery automation: You are a marketing automation specialist. Task: Design an automation flow for abandoned carts with three messages and timing. Output format: plain-text outline.
82. Product discount optimization: You are a pricing strategist. Task: Determine optimal discount depth for top products to maximize margin and volume this month. Output format: table and recommendation.
83. Geo demand signals: You are a market analyst. Task: Identify high-potential regions for expansion based on last 6 months sales and demographics. Output format: bullet list.
84. Shipping delay alerts: You are a logistics analyst. Task: Produce real-time alerts for shipments delayed beyond threshold and suggested mitigations. Output format: bullet list.
85. Return rate by category: You are a data analyst. Task: Break down return rate by category and propose category-level actions. Output format: table.
86. Order value clustering: You are a data scientist. Task: Perform simple clustering of orders by value and frequency to identify segments for promotions. Output format: bullet list.
87. VIP customer plan: You are a CRM strategist. Task: Propose a VIP program for top customers with tiers and rewards. Output format: bullet list.
88. Checkout form autofill: You are a UX designer. Task: Recommend autofill and autofill-safe practices for checkout forms to speed up purchases. Output format: bullet list.
89. Product page recommendations: You are a merchandising analyst. Task: Generate three recommended product groupings on product pages to boost cross-sell. Output format: bullet list.
90. New visitor uplift: You are a growth hacker. Task: Propose three tactics to increase new visitor to buyer conversion this week. Output format: bullet list.
91. Order cancellation trends: You are a data analyst. Task: Analyze order cancellations by reason and propose prevention steps. Output format: bullet list.
92. Tax compliance snapshot: You are a finance auditor. Task: Provide a quick tax compliance snapshot by region for the current month. Output format: bullet list.
93. Product bundle performance: You are a merchandising analyst. Task: Evaluate performance of product bundles versus individual SKUs and suggest optimal bundle compositions. Output format: table.
94. Cart value decay: You are a data scientist. Task: Model how cart value decays across user sessions and propose timing for re-engagement. Output format: short report.
95. Inventory shortage risk: You are a supply chain planner. Task: Identify SKUs at risk of stockouts in the next 14 days and suggest procurement actions. Output format: bullet list.
96. A/B test plan: You are an experimentation lead. Task: Outline a rigorous A/B test plan for a homepage change with hypotheses, metrics, sample size, and duration. Output format: outline.
97. LTV risk warning: You are a finance data analyst. Task: Flag cohorts with declining LTV and provide mitigation actions. Output format: bullet list.
98. Data quality check: You are a data operations analyst. Task: Run a data quality check on Shopify revenue data, flag anomalies, and propose fixes. Output format: short report.
99. Dashboard blueprint: You are a dashboard designer. Task: Draft a KPI dashboard layout for Shopify KPI and dashboard for Shopify stores with sections, metrics, and data sources. Output format: bullet list.
100. Executive snapshot: You are a senior business analyst. Task: Produce a one-page executive snapshot of all key KPIs, trends, and recommendations for Shopify stores this month. Output format: concise summary.

Best Practices

• Keep prompts specific to Shopify data sources and avoid generic analytics language.
• Aim for outputs that are easily pasted into dashboards or reports, with minimal post-processing.
• Re-run prompts after data updates to confirm accuracy and capture latest trends.
• Combine multiple prompts to build a layered dashboard where each prompt feeds a card or widget.
• Always include a concise interpretation line to help stakeholders act on the numbers.

Common Mistakes to Avoid

• Using vague metrics like performance without defining what to measure.
• Forgetting data recency or scope in date ranges, leading to stale insights.
• Asking for outputs without a defined format, causing inconsistencies across dashboards.
• Overloading prompts with too many KPIs, reducing clarity and actionability.
• Neglecting validation steps to verify outputs against source data.

FAQ

What is the best way to start using these Claude prompts for Shopify KPI dashboards?

Start with a core set of prompts that cover revenue, conversion, and customer behavior, then expand with complementary prompts for channels and segmentation.

Can these prompts handle real-time Shopify data?

They are designed for regular structured data extracts; for real-time data, run prompts on fresh extracts or connect to streaming data if your workflow supports it.

How should I format outputs for dashboards?

Prefer plain text tables or bullet lists with clear headings, so you can paste directly into dashboards without extra formatting.

What should I do if numbers don’t match my Shopify reports?

Check data alignment: date zones, time zones, currency, and data export scope; adjust the prompt constraints accordingly.

Are these prompts suitable for agencies serving multiple Shopify stores?

Yes. Use placeholders for store identifiers and generate per-store outputs or aggregate across stores with a store filter.

How can I verify the accuracy of the prompts’ outputs?

Cross-validate with Shopify reports, sample data checks, and, where possible, manual spot checks of a few orders.