User Prompts

# User Prompts for Tableau AI

## Prompt 1: Executive Sales Dashboard
"Build me an executive sales dashboard using my sales dataset (order_id, product, category, sub_category, sales, quantity, discount, profit, region, state, city, order_date, ship_date, segment, ship_mode). Include: (1) A KPI banner showing total sales, total profit, profit margin %, and YoY growth, (2) Monthly sales trend line with a 3-month moving average overlay, (3) Sales and profit by region as a horizontal bar chart with profit ratio color encoding, (4) Top 10 products by profit with their sales volume, (5) Customer segment breakdown. Make it filterable by date range, region, and category. Use a clean, corporate color palette."

## Prompt 2: Geographic Performance Analysis
"I have store performance data across 500 locations in the US (store_id, latitude, longitude, state, city, monthly_revenue, monthly_foot_traffic, store_size_sqft, employee_count, customer_satisfaction_score, store_type). Create a geographic analysis that: (1) Maps all stores with size/color encoding for revenue and satisfaction, (2) Identifies regional clusters of high and low performers, (3) Shows a scatter plot of store size vs revenue to find efficiency outliers, (4) Creates a heat map of revenue density by state, (5) Builds a dual-axis chart showing the relationship between foot traffic and revenue over time. Help me identify which stores need attention and which regions to expand in."

## Prompt 3: Customer Segmentation Visualization
"Using my customer data (customer_id, total_purchases, total_spend, avg_order_value, days_since_last_purchase, number_of_returns, preferred_category, acquisition_channel, lifetime_months), help me create an RFM (Recency, Frequency, Monetary) segmentation dashboard: (1) Score customers on Recency, Frequency, and Monetary value (1-5 scale each), (2) Create customer segments (Champions, Loyal, At Risk, Lost, etc.), (3) Visualize segment distribution with a treemap, (4) Show average metrics per segment in a comparative table, (5) Plot customer migration between segments over quarters, (6) Recommend targeted actions for each segment."

## Prompt 4: Supply Chain Monitoring Dashboard
"Design a real-time supply chain monitoring dashboard with my logistics data (shipment_id, origin, destination, carrier, ship_date, expected_delivery, actual_delivery, weight, cost, status, delay_reason). I need: (1) On-time delivery rate KPI with trend, (2) A Gantt-style chart showing shipments in transit with expected vs actual timelines, (3) Delay analysis — breakdown by carrier, route, and reason, (4) Cost per kg trends by carrier with anomaly highlighting, (5) Heat map of delivery performance by route (origin-destination pairs), (6) Alert indicators for shipments likely to be delayed. Include filters for date range, carrier, and status."

## Prompt 5: A/B Test Results Visualization
"I ran an A/B test on our checkout flow with the following data (user_id, variant [A/B], converted [0/1], revenue, time_on_page, pages_viewed, device_type, traffic_source, test_date). Create a comprehensive test results dashboard: (1) Conversion rate comparison with confidence intervals (show if statistically significant at 95%), (2) Revenue per visitor comparison with significance testing, (3) Segment analysis — break down results by device type and traffic source, (4) Daily conversion rate trend for both variants to check for novelty effects, (5) Funnel visualization showing drop-off at each step for A vs B, (6) Sample size and power analysis visualization. Make sure the dashboard clearly communicates whether we should ship variant B."