Customer Purchase Data Analysis Report

I'll start by loading the relevant skills and examining the data

Now let me load the semantic SQL skill and the slide reference for the PPT workflow.

Excellent! I have a thorough understanding of the data and the workflow. Let me start with Phase 1 — Explore to understand the data thoroughly, then generate charts, and finally the PPT.

Let me explore the customer purchase data first.

I see - the measure names use their SQL identifiers. Let me check the correct names from the schema.

From the schema, the SQL identifiers are:

• unique_customers_purchase (not unique_customers)
• total_purchases (correct)
• average_order_value (correct)
• average_purchases_per_customer (correct)
• average_age_purchase (not average_age)
• average_income_purchase (not average_income)
• email_subscriber_count (not email_subscribers)
• total_income_purchase (not total_income)

Let me re-run with correct identifiers

Great data! Let me now explore more dimensions - income, email/promo status, and purchase intent.

Now let me explore the engagement metrics (session duration, clicks, page views) and purchase behaviors more deeply.

Excellent! I now have a comprehensive understanding of the data. Let me now proceed to Phase 2 — Chart Generation. I'll create the key charts for the PPT.

Let me plan the charts:

1. Overall KPI dashboard - big numbers for overview
2. Gender distribution - bar chart (customers, purchases, avg order value)
3. Age group segmentation - bar chart (customers & purchases by age band)
4. Income bracket analysis - bar chart
5. Marketing impact - email/promo/purchase intent comparison
6. Age + engagement - dual axis chart

Let me start with Phase 2 charts:

The CTE approach for age bucketing hit a Cube issue - the CTE reference isn't accessible as a cube name. Let me use a different approach - I'll query the raw age data and bucket in the chart SQL directly without CTE, or use a simpler approach

Excellent! Now let me create the remaining charts - purchase intent, income bracket, and purchase intent vs promo.

All 6 charts are generated. Now let me proceed to the PPT report pipeline. First, I need to write the summary and outline, then select a template.

Now I need to ask the user to select a template for the PPT

The user chose template dark-green. Let me proceed with the report pipeline

Guideline succeeded. Now I proceed immediately to report_presentation_generate:

I'll load the dashboard skill for this.

I now have the explore info. Let me get the explore details and then load the embed-chart skill for creating chart JSONs

The explore details show the anchor is customer_purchase_u9m. The analysis charts we already created via save_embed_chart should have generated charts/<slug>.json files. Let me check what's there and also load the embed-chart references for writing chart JSONs

Now let me check the sample chart JSON files and also check what chart files were produced by save_embed_chart

Now I have a good understanding of the chart JSON format. The save_embed_chart calls from earlier should have generated charts/<slug>.json files. Let me also check if the existing analysis charts are on disk, and then write the dashboard design doc.

Let me first write the design doc, then create the needed chart JSONs and the dashboard JSON.

Now I need to create the 4 big number KPI charts (new) and reuse the 6 analysis charts. Let me create the KPI charts as JSON files and also check what files the analysis phase already generated.

Now let me verify the KPI charts and check what analysis chart JSON files already exist from the save_embed_chart calls.

All 4 KPI charts verified. Now let me check if the analysis charts from save_embed_chart are on disk as charts/<slug>.json files:

All 10 charts (4 new KPI + 6 analysis) are verified. Now let me write the dashboard JSON:

I need to fix the markdown tiles — they need min height of 3. Let me also adjust the y positions to account for the extra height.

Now let me report the deliverables