I'll start by loading the relevant skills for this analysis and report generation task.
Now let me explore the data schema to understand the table structure
Excellent! I have a rich e-commerce consumer behavior dataset. Let me now explore the data to understand the key metrics before generating charts
Excellent data! Now let me load the slide reference for the presentation workflow, and then generate the charts
Now I have a comprehensive understanding of the data and the slide workflow. Let me write the summary and outline, then generate the charts.
First, let me generate the key charts for the PPT
Now let me generate the charts. I'll do this in the explore phase first, then the chart phase
Good, now I have all the data I need. Let me now generate the charts in Phase 2
I need to fix the big number chart - it only accepts one column. Let me fix and retry
All 10 charts generated successfully. Now I need to proceed with the presentation workflow. Let me ask the user for template selection as required by the slide workflow.
The user selected the dark-green template. Let me proceed with the report guideline and presentation generation.
The guideline succeeded with 9 usable charts across 10 pages. The big_number chart was auto-filtered (expected behavior). Now let me proceed to generate the presentation
The presentation has been generated. Let me deliver it via result_reporter
Bayes: All done! Your E-Commerce Consumer Behavior Analysis PPT has been generated. Here's a summary of what's inside:
📊 Report Summary — 10 slides
| Slide | Topic | Key Insight |
|---|---|---|
| 1 | Cover | E-Commerce Consumer Behavior Analysis |
| 2 | Executive Summary | $63K total revenue, 500 customers, $126 avg order value |
| 3 | Revenue by Category | Home & Kitchen leads ($15.1K), Books 2nd ($10.7K), Toys has room to grow |
| 4 | Customer Segments | Regular (37%) and New (33%) drive revenue; Loyal segment highest AOV at $133 |
| 5 | Region & Gender | East region leads; Male customers slightly outspend females across all regions |
| 6 | Category × Gender | Male: Home & Kitchen + Clothing; Female: Books + Beauty — clear targeting opportunities |
| 7 | Temporal Trends | Q3 is strongest; Tuesday peaks, Sunday troughs — weekday-heavy pattern |
| 8 | Payment & Promo | Debit Card #1; Promo users have 13% lower AOV — a price-sensitive segment |
| 9 | Repeat Purchase | 40.8% repeat rate with higher AOV ($132 vs $122) — loyalty opportunity |
| 10 | Recommendations | Grow VIP/Loyal segments, expand top categories, gender-specific marketing, Q3 campaigns |
The file is E-Commerce_Consumer_Behavior_Analysis.pres — ready to download and present.
I'll load the dashboard skill to build an interactive embed dashboard.
Now let me load the design template, references, and sample dashboard files.
Now let me explore the available data via dashboardmaker to get the exact field IDs
Let me also read the full output to get the complete dimensions list
I have all the field IDs now. Let me create the design doc, then generate the chart JSONs and the dashboard. I already have the chart analysis charts from the earlier Phase 2 run. Let me check what's available on disk and create the dashboard.
I need to generate new embed charts for the dashboard since the ones from the PPT analysis are save_embed_chart outputs that should be in charts/ directory. Let me also generate a few more chart types per the UX guidelines (pie chart, etc.)
Let me load the embed-chart skill for chart JSON rules, and then create the dashboard.
I already have all the analysis charts from the PPT phase generated via save_embed_chart. Let me check what's in the charts directory and then create the dashboard JSON. Since the analysis charts were already generated via save_embed_chart, the charts/ directory should already have the JSON files. Let me create the dashboard design doc and then the dashboard itself.
First, let me write the design doc
Now I need to generate the additional chart JSON files that are needed. I already have the analysis charts from the PPT step. Let me generate the pie chart for category share, and the big number KPI charts using `save_embed_chart
All charts are generated. Now let me check what chart files exist and then build the dashboard JSON
All charts are on disk. Now let me read the sample dashboard JSON and write the dashboard
Now let me check the pie chart structure and then write the dashboard JSON
Now let me write the dashboard JSON
Now let me verify the dashboard
The markdown tiles need min height of 3. Let me fix these.
Dashboard verified successfully! Now let me deliver it
Bayes: The interactive E-Commerce Consumer Behavior Dashboard is ready! Here's what's inside:
📊 Dashboard Overview
Single-page interactive BI dashboard with 12 charts organized into 4 sections:
🏆 Hero KPIs (top band)
| KPI | Value |
|---|---|
| Total Revenue | $63,067.48 |
| Transactions | 500 |
| Unique Customers | 500 |
| Avg Order Value | $126.13 |
📦 Category & Segment Analysis
- Revenue by Category (bar chart) — Home & Kitchen #1 at $15,120
- Category Revenue Share (pie chart) — visual breakdown of all 8 categories
- Revenue by Segment (bar chart) — Regular 37%, New 33%, VIP 22%, Loyal 8%
- Repeat Purchase (bar chart) — repeat customers drive 40.8% of revenue
🌍 Regional & Temporal Trends
- Revenue by Region & Gender (bar chart) — East leads, male outspends female
- Revenue by Day of Week (bar chart) — Tuesday peak, Sunday trough
💳 Payment & Promo Behavior
- Revenue by Payment Method (bar chart) — Debit Card #1
- Promo Code Impact (bar chart) — promo users have 13% lower AOV
🔍 Filters (top of dashboard)
- Product Category — filter all charts by category
- Customer Segment — filter by Regular / New / VIP / Loyal
- Purchase Quarter — filter by Q1–Q4
