I'll start by loading the required skills and examining the data
Now let me explore the data to understand its scope before planning the analysis.
Excellent! I have a comprehensive understanding of the data and the workflow. Let me start with Phase 1 — Explore to understand the supermarket transaction data thoroughly.
The table supermarket_transactions_sheet1_40f has:
- Measures: total_amount, category quantities (Bakery, Beverages, Canned Goods, Dairy, Dry Goods, Frozen Foods, Household, Meat, Produce, Snacks), used_discount
- Dimensions: customer_gender, member_tier, payment_method, transaction_datetime, hour_of_day, weekday, customer_age, bought_on_sale, num_items, has_dairy, has_frozen, has_meat, has_produce
Let me explore the data
No count measure available — let me adjust. I'll query without the count measure.
Excellent! Now let me explore the category-level data and customer age distribution.
Now let me get more customer-level insights and the distinct customer count.
Excellent! I now have a comprehensive understanding of the data. Let me now proceed to Phase 2 — Chart generation and create the key charts for the PPT report.
I need to use the correct alias for the date_trunc column. Let me fix the aliases.
Now let me create the remaining charts — revenue by gender, member tier, payment method, and category breakdown.
I need to fix the aliases for the category chart and create the remaining charts. Let me continue with fixed aliases
The CTE approach for age group fails. Let me use a different approach - I'll query the raw age data and use the post-aggregation chart approach with a simpler SQL query, or use the age data directly.
Charts 4-12 are all done. Now I need to create the Age Group chart using the embed-chart fallback approach since the Cube SQL CASE+GROUP BY approach doesn't work directly. Let me write the age group chart as a static ECharts JSON.
Actually, I already have the age_grouped data from the explore phase. Let me try the save_embed_chart approach with the raw age data as a regular bar chart. But since there are many age values, let me use the embed-chart skill's write_file fallback approach.
Wait - I can try a different SQL approach. Let me try using the raw customer_age data with save_embed_chart.
Excellent! All charts are created. Now let me write the summary and outline for the PPT report, then proceed with the template selection and report pipeline.
Now let me check which chart config files were created and proceed with template selection
All 12 charts are on disk. Now let me start the template selection process
Great choice! The user selected template "gray" (8281686b-ee05-4bdb-8150-56fe18799ef1). Let me proceed with the report pipeline.
Guideline succeeded. Now I'll proceed directly to generating the presentation.
Bayes: Your Supermarket Transactions Analysis Report is ready! Here's a summary of what's inside:
📊 Report Overview — 11 Slides
Data: Jan–Jul 2025 | 674 customers | $72,167 total revenue
Pages:
| Slide | Topic | Key Insight |
|---|---|---|
| 1 | Cover | — |
| 2 | Executive Summary | 91% revenue from sale items; 60% from Non-Members |
| 3 | Monthly Revenue Trend | Peaked at $14,153 in May, then declined |
| 4 | Weekly & Hourly Patterns | Saturdays & 2–4 PM are peak periods |
| 5 | Revenue by Sale Status | Sale items dominate at $65,718 vs $6,448 regular |
| 6 | Customer Demographics | Equal gender split; 60+ customers spend most |
| 7 | Member Tier Analysis | Non-Members = 60% revenue but 0% discount usage |
| 8 | Discount Usage | Silver members lead discount usage (113 uses) |
| 9 | Payment Method | Credit Cards 40%, Debit Cards 26%, Mobile only 9% |
| 10 | Product Category | Household, Beverages, Dairy top the list |
| 11 | Recommendations | Convert Non-Members, optimize staffing, promote mobile payments |
The file Supermarket_Transactions_Analysis.pres is ready for download.
Let me load the dashboard skill to follow the proper workflow.
Let me follow the dashboard pipeline step by step. First, I'll load the design template and explore the available data.
Now let me write the dashboard design doc and explore the available data.
Now let me explore the data source to get the correct field IDs
I have the field IDs. Now let me check which existing chart JSON files are already stored under charts/ from the prior analysis, then create the missing ones and build the dashboard.
All 12 charts already exist in charts/ directory. Now I need to create the two KPI big_number charts that don't exist yet (total_revenue and discount_usage KPIs). Let me check the content of the existing charts to understand the fieldId format.
I see the tableName is the cube ID. Now I need to create the two KPI big_number charts using save_embed_chart and then build the dashboard JSON. Let me create the KPIs and the dashboard
Let me create the two KPI big_number charts using save_embed_chart, then build the dashboard. Since the charts already exist from analysis, I just need to create the two big_number KPIs
Both KPIs created. Now let me read the sample chart JSON files to understand the structure for the existing charts, then build the dashboard JSON
Now I have a good understanding of the chart JSON structure. Let me verify the existing chart JSONs have the right structure, and check the KPI charts too.
All charts are properly structured. Now let me build the dashboard JSON file
Now let me verify the dashboard
The KPI tiles are at y=4,h=2 and the monthly trend chart is at y=4 sharing the same y-band. I need to move the monthly trend chart to start right after the KPIs end (y=6).
Dashboard verified successfully! Now let me report the deliverable.
Bayes: Your Supermarket Transactions Dashboard is ready! Here's what's included:
📊 Dashboard Layout — 4 Sections, 15 Tiles
1️⃣ Overview (top band)
- Total Revenue KPI: $72,167
- Discount Usage KPI: 291 items
- Monthly Revenue Trend line chart
2️⃣ Customer Analysis
- Revenue by Gender (pie) — Female 51% vs Male 49%
- Revenue Share by Member Tier (pie) — Non-Members 60%
- Revenue by Age (bar chart) — 60+ leads at $21,733
3️⃣ Time Patterns
- Revenue by Weekday — Saturday highest ($11,831), Tuesday lowest
- Revenue by Hour — Peak 2–4 PM, $11,502 at 3 PM
4️⃣ Products & Payments
- Category Sales by Gender — Household, Beverages, Dairy top
- Payment Method Share (pie) — Credit Cards 40%, Debit 26%
🔍 Filters
- Date range filter — filter by transaction date
- Customer gender filter — filter by gender
The dashboard is interactive — you can apply filters to explore the data dynamically.
