# Retail Sales Performance

**Creator:** Wasu
**Version:** 1
**Version date:** 2026-06-18
**Exported from:** Skill Hub

---

# Retail Sales Performance — Skill

## Triggers
- **"create sales report"** → produce Excel file (see Excel section below)
- **"show sales dashboard"** → render interactive dashboard widget in chat (see Dashboard section below)

## Overview
Produces a single `.xlsx` file with **4 sheets**, each populated from the retail
PostgreSQL database via the `retail-db` MCP tool.

Always also read `/mnt/skills/public/xlsx/SKILL.md` before writing code.

---

## Step-by-step execution

### Step 1 — Run 4 SQL queries via MCP

Use `retail-db:run_select` for each query below.
SQL comments must use `/* */` syntax — `--` is rejected by the MCP tool.

---

#### Sheet 1 · Monthly Sales Summary

```sql
SELECT
    (txn_date_key / 100) % 100                                  AS month,
    COUNT(*)                                                     AS total_transactions,
    SUM(net_amount)                                              AS total_revenue,
    SUM(qty_sold)                                                AS total_units_sold,
    COUNT(*) FILTER (WHERE customer_type = 'NEW')                AS new_customers,
    COUNT(*) FILTER (WHERE customer_type = 'RETURNING')          AS returning_customers,
    COUNT(*) FILTER (WHERE txn_status = 'RETURN')                AS returns,
    SUM(net_amount) FILTER (WHERE txn_status != 'RETURN')        AS net_revenue,
    ROUND(SUM(net_amount) / NULLIF(COUNT(*), 0), 2)              AS avg_basket_size,
    400000                                                       AS target_revenue,
    ROUND(
        SUM(net_amount) FILTER (WHERE txn_status != 'RETURN')::NUMERIC
        / 400000, 6
    )                                                            AS achievement_pct
FROM fact_transaction
WHERE txn_date_key >= 20260101
GROUP BY ROLLUP(1)
ORDER BY 1 NULLS LAST
```

**Notes:**
- Row where `month IS NULL` = YTD total (ROLLUP grand total)
- Display `month` as Thai month name: `{1:"มกราคม", 2:"กุมภาพันธ์", 3:"มีนาคม", 4:"เมษายน", 5:"พฤษภาคม", 6:"มิถุนายน"}` or `"YTD Total"` for null
- `achievement_pct` = net_revenue / (target × months elapsed) for YTD row

---

#### Sheet 2 · Slow-Moving Product Tracker

```sql
SELECT * FROM (
    WITH sales_flags AS (
        SELECT
            product_key,
            COUNT(*)                AS total_transactions_30d,
            SUM(qty_sold)           AS units_sold_30d,
            MAX(txn_date_key)       AS last_sale_date_key
        FROM fact_transaction
        WHERE txn_date_key >= (
            SELECT MAX(txn_date_key) - 100
            FROM fact_transaction
        )
          AND txn_status != 'RETURN'
        GROUP BY product_key
    )
    SELECT
        dp.product_code,
        dp.product_name,
        dc.category_name,
        dp.stock_qty,
        COALESCE(sf.units_sold_30d, 0)          AS units_sold_30d,
        sf.last_sale_date_key,
        CASE
            WHEN sf.last_sale_date_key IS NULL THEN NULL
            ELSE (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT, 'YYYYMMDD'))
        END                                      AS days_since_last_sale,
        dp.unit_cost,
        dp.unit_price,
        ROUND(dp.stock_qty * dp.unit_cost, 2)    AS stock_value,
        ROUND(COALESCE(sf.units_sold_30d, 0)::NUMERIC / 30, 3) AS velocity,
        CASE
            WHEN COALESCE(sf.units_sold_30d, 0) = 0 THEN 999
            ELSE ROUND(dp.stock_qty::NUMERIC / (sf.units_sold_30d::NUMERIC / 30), 1)
        END                                      AS days_of_stock,
        CASE
            WHEN sf.last_sale_date_key IS NULL THEN 'CRITICAL'
            WHEN (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT, 'YYYYMMDD')) > 60 THEN 'CRITICAL'
            WHEN (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT, 'YYYYMMDD')) > 30 THEN 'WARNING'
            WHEN (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT, 'YYYYMMDD')) > 14 THEN 'WATCH'
            ELSE 'ACTIVE'
        END                                      AS level,
        CASE
            WHEN sf.last_sale_date_key IS NULL THEN 'พิจารณาลดราคา/คืนสินค้า'
            WHEN (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT, 'YYYYMMDD')) > 60 THEN 'พิจารณาลดราคา/คืนสินค้า'
            WHEN (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT, 'YYYYMMDD')) > 30 THEN 'จัดโปรโมชั่นด่วน'
            WHEN (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT, 'YYYYMMDD')) > 14 THEN 'ติดตามยอดขาย'
            ELSE 'ปกติ'
        END                                      AS action
    FROM dim_product dp
    LEFT JOIN dim_category dc
        ON dp.category_key = dc.category_key
    LEFT JOIN sales_flags sf
        ON dp.product_key = sf.product_key
    WHERE dp.is_active = TRUE
) sub
ORDER BY units_sold_30d ASC, COALESCE(days_since_last_sale, 999) DESC
```

**Level color coding (apply as cell fill):**
| Level    | Fill hex  |
|----------|-----------|
| CRITICAL | `#FCEBEB` |
| WARNING  | `#FAEEDA` |
| WATCH    | `#E6F1FB` |
| ACTIVE   | `#EAF3DE` |

---

#### Sheet 3 · Staff Performance Leaderboard

```sql
WITH txn_totals AS (
    SELECT
        ds.staff_key,
        COUNT(*)                                            AS total_transactions,
        SUM(ft.net_amount)                                  AS total_revenue,
        SUM(ft.qty_sold)                                    AS units_sold,
        COUNT(*) FILTER (WHERE ft.txn_status = 'RETURN')   AS returns_handled,
        COUNT(*) FILTER (WHERE fc.customer_type = 'NEW')   AS new_customers_acquired,
        COUNT(*) FILTER (WHERE ft.has_upsell = TRUE)       AS upsell_count
    FROM fact_transaction ft
    LEFT JOIN dim_staff ds
        ON ft.staff_key = ds.staff_key
    LEFT JOIN fact_customer fc
        ON ft.customer_key = fc.customer_key
    WHERE ft.txn_date_key >= 20260101
      AND ft.txn_status != 'RETURN'
    GROUP BY ds.staff_key
)
SELECT
    ds.staff_code                                               AS staff_id,
    ds.full_name                                                AS staff_name,
    db.branch_name                                              AS branch,
    COALESCE(tt.total_transactions, 0)                          AS total_transactions,
    COALESCE(tt.total_revenue, 0)                               AS total_revenue,
    COALESCE(tt.units_sold, 0)                                  AS units_sold,
    CASE
        WHEN COALESCE(tt.total_transactions, 0) > 0
        THEN ROUND(tt.total_revenue / tt.total_transactions, 2)
        ELSE 0
    END                                                         AS avg_basket_size,
    COALESCE(tt.returns_handled, 0)                             AS returns_handled,
    COALESCE(tt.new_customers_acquired, 0)                      AS new_customers_acquired,
    CASE
        WHEN COALESCE(tt.total_transactions, 0) > 0
        THEN ROUND(tt.upsell_count::NUMERIC / tt.total_transactions, 6)
        ELSE 0
    END                                                         AS upsell_rate,
    150000                                                      AS target_revenue,
    ROUND(COALESCE(tt.total_revenue, 0)::NUMERIC / 150000, 6)  AS achievement_pct
FROM dim_staff ds
LEFT JOIN dim_branch db
    ON ds.branch_key = db.branch_key
LEFT JOIN txn_totals tt
    ON ds.staff_key = tt.staff_key
WHERE ds.is_active = TRUE
ORDER BY total_revenue DESC, upsell_rate DESC
```

**Computed columns (add in Python after query):**

`short_summary` — classify each row:
```
total_transactions == 0 or total_revenue == 0          → "Inactive"
total_transactions < 60                                → "ข้อมูลน้อย"
achievement_pct >= 1.0 and upsell_rate >= 0.20         → "Top performer"
total_transactions >= 250 and achievement_pct < 0.50   → "High volume, low revenue"
upsell_rate < 0.05                                     → "Low engagement"
total_revenue >= 100000 and new_customers_acquired <= 5 → "High revenue, low acquisition"
else                                                   → "Developing"
```

`action` — management suggestion:
```
"Top performer"                 → "ขยายโซน / ให้เป็น mentor"
"High volume, low revenue"      → "ทบทวนเทคนิคขาย / coaching"
"Inactive"                      → "ตรวจสอบด่วน"
"Low engagement"                → "กำหนด KPI upsell"
"High revenue, low acquisition" → "เพิ่มเป้าลูกค้าใหม่"
"ข้อมูลน้อย"                    → "ติดตามเพิ่มเติม"
"Developing"                    → "ติดตามผล / support"
```

---

#### Sheet 4 · Category Performance Analysis

```sql
SELECT
    *,
    CASE
        WHEN revenue_contribution >= 0.30 AND avg_margin_pct >= 0.25 THEN 'Star category'
        WHEN revenue_contribution >= 0.20                             THEN 'High volume'
        WHEN avg_margin_pct >= 0.35                                   THEN 'High margin — low volume'
        WHEN return_rate >= 0.08                                      THEN 'High return risk'
        WHEN revenue_contribution < 0.08                              THEN 'Low contribution'
        ELSE                                                               'Stable'
    END                                                               AS summary,
    CASE
        WHEN revenue_contribution >= 0.30 AND avg_margin_pct >= 0.25 THEN 'ขยาย SKU / เพิ่ม shelf space'
        WHEN revenue_contribution >= 0.20                             THEN 'ทบทวน margin — volume ดีแต่กำไรต่ำ'
        WHEN avg_margin_pct >= 0.35                                   THEN 'เพิ่ม visibility / จัดโปรโมชั่น'
        WHEN return_rate >= 0.08                                      THEN 'ตรวจสอบคุณภาพสินค้า / นโยบาย return'
        WHEN revenue_contribution < 0.08                              THEN 'พิจารณาลด SKU / เพิ่มสินค้าใหม่'
        ELSE                                                               'รักษา performance / ติดตามต่อ'
    END                                                               AS suggest
FROM (
    SELECT
        dc.category_name                                                          AS category,
        COUNT(DISTINCT dp.product_key)                                            AS total_skus,
        COUNT(ft.txn_key)                                                         AS total_transactions,
        ROUND(SUM(ft.net_amount), 2)                                              AS total_revenue,
        SUM(ft.qty_sold)                                                          AS total_units_sold,
        COUNT(ft.txn_key) FILTER (WHERE ft.txn_status = 'RETURN')                AS returns,
        ROUND(
            COUNT(ft.txn_key) FILTER (WHERE ft.txn_status = 'RETURN')::NUMERIC
            / NULLIF(COUNT(ft.txn_key), 0), 6)                                   AS return_rate,
        ROUND(AVG(dp.margin_pct), 4)                                              AS avg_margin_pct,
        ROUND(SUM(ft.net_amount) * AVG(dp.margin_pct), 2)                        AS gross_profit,
        ROUND(
            SUM(ft.net_amount)::NUMERIC
            / NULLIF(SUM(SUM(ft.net_amount)) OVER (), 0), 6)                     AS revenue_contribution
    FROM fact_transaction ft
    LEFT JOIN dim_product dp
        ON ft.product_key = dp.product_key
    LEFT JOIN dim_category dc
        ON dp.category_key = dc.category_key
    WHERE ft.txn_date_key >= 20260101
    GROUP BY dc.category_name
) base
ORDER BY total_revenue DESC
```

---

### Step 2 — Build Excel file with openpyxl

Use openpyxl. Font: **Arial 10**. Do NOT use pandas to_excel (no formatting control).

#### General sheet formatting rules
- Row 1 = header row: bold, white text (`FFFFFF`), dark blue fill (`1F3864`), center-aligned, frozen
- Data rows: alternating white / light gray (`F2F2F2`)
- Auto-fit column widths (min 10, max 50)
- All percentage columns: format as `0.00%`
- Currency columns (revenue, profit, cost, basket): format as `#,##0.00`
- Count columns ≥ 1000: format with thousands separator `#,##0`
- Thai text columns: UTF-8 — openpyxl handles natively

#### Sheet 1 — "Monthly Sales Summary"
Columns: Month | Total Transactions | Total Revenue | Units Sold | New Customers | Returning Customers | Returns | Net Revenue | Avg Basket Size | Target Revenue | Achievement %

- `month` null row → `"YTD Total"`: bold, light yellow fill (`FFFDE7`)
- Achievement %: `0.00%` format; color red if < 80%, amber if 80–99%, green if ≥ 100%

#### Sheet 2 — "Slow-Moving Products"
Columns: Product Code | Product Name | Category | Stock Qty | Units Sold (30d) | Last Sale Date | Days Since Last Sale | Unit Cost | Unit Price | Stock Value | Velocity (u/day) | Days of Stock | Level | Action

- `last_sale_date_key`: format YYYYMMDD int → `YYYY-MM-DD` string
- `days_of_stock` = 999 → display as `"∞"`
- `level` cell fill color per color table above
- `velocity`: `0.000` number format

#### Sheet 3 — "Staff Performance"
Columns: Staff ID | Staff Name | Branch | Total Transactions | Total Revenue | Units Sold | Avg Basket Size | Returns Handled | New Customers | Upsell Rate | Target Revenue | Achievement % | Short Summary | Action

- Upsell Rate, Achievement %: `0.00%` format
- Total Revenue: bold
- Short Summary cell fill:
  ```
  Top performer              → #EAF3DE (green)
  High volume, low revenue   → #E6F1FB (blue)
  Inactive                   → #FCEBEB (red)
  Low engagement             → #FAEEDA (amber)
  High revenue, low acq.     → #EEEDFE (purple)
  Developing / ข้อมูลน้อย   → #F1EFE8 (gray)
  ```

#### Sheet 4 — "Category Performance"
Columns: Category | Total SKUs | Total Transactions | Total Revenue | Units Sold | Returns | Return Rate | Avg Margin % | Gross Profit | Revenue Contribution | Summary | Suggest

- Return Rate, Avg Margin %, Revenue Contribution: `0.00%` format
- Gross Profit: `#,##0.00` format
- Summary cell fill:
  ```
  Star category              → #EAF3DE
  High volume                → #E6F1FB
  High margin — low volume   → #EEEDFE
  High return risk           → #FCEBEB
  Low contribution           → #FAEEDA
  Stable                     → #F1EFE8
  ```

---

### Step 3 — File naming & output

Save to `/mnt/user-data/outputs/RetailSales_Report_YYYYMMDD.xlsx`
where `YYYYMMDD` = today's date (`datetime.date.today().strftime('%Y%m%d')`).

Then call `present_files` with the output path.

---

## Dashboard (chat widget) — trigger: "show sales dashboard"

**Do not create an Excel file.** Render using `visualize:show_widget` only.

### Step 1 — Run 4 SQL queries via MCP (same tool, same `/* */` comment rule)

#### Query A — Monthly trend
```sql
SELECT
    (txn_date_key / 100) % 100                                              AS month,
    COUNT(*)                                                                 AS total_transactions,
    SUM(net_amount)                                                          AS total_revenue,
    SUM(net_amount) FILTER (WHERE txn_status != 'RETURN')                   AS net_revenue,
    COUNT(*) FILTER (WHERE txn_status = 'RETURN')                           AS returns,
    ROUND(
        SUM(net_amount) FILTER (WHERE txn_status != 'RETURN')::NUMERIC
        / 400000, 4)                                                         AS achievement_pct
FROM fact_transaction
WHERE txn_date_key >= 20260101
GROUP BY 1
ORDER BY 1
```

#### Query B — Slow-moving product health
```sql
WITH sales_flags AS (
    SELECT
        product_key,
        MAX(txn_date_key)   AS last_sale_date_key
    FROM fact_transaction
    WHERE txn_status != 'RETURN'
    GROUP BY product_key
)
SELECT
    COUNT(*)                                                                                                              AS total_active_skus,
    COUNT(*) FILTER (WHERE sf.last_sale_date_key IS NULL)                                                                AS no_sales,
    COUNT(*) FILTER (WHERE (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT,'YYYYMMDD')) > 60)                       AS critical,
    COUNT(*) FILTER (WHERE (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT,'YYYYMMDD')) BETWEEN 31 AND 60)          AS warning,
    COUNT(*) FILTER (WHERE (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT,'YYYYMMDD')) BETWEEN 15 AND 30)          AS watch,
    COUNT(*) FILTER (WHERE (CURRENT_DATE - TO_DATE(sf.last_sale_date_key::TEXT,'YYYYMMDD')) <= 14)                      AS active
FROM dim_product dp
LEFT JOIN sales_flags sf ON dp.product_key = sf.product_key
WHERE dp.is_active = TRUE
```

#### Query C — Staff leaderboard (top 6 by revenue)
```sql
SELECT
    ds.full_name                                                    AS staff_name,
    COALESCE(SUM(ft.net_amount), 0)                                 AS total_revenue,
    COUNT(ft.txn_key)                                               AS total_transactions,
    ROUND(
        COALESCE(SUM(ft.net_amount), 0)::NUMERIC / 150000, 4)      AS achievement_pct
FROM fact_transaction ft
LEFT JOIN dim_staff ds ON ft.staff_key = ds.staff_key
WHERE ft.txn_date_key >= 20260101
  AND ft.txn_status != 'RETURN'
GROUP BY ds.full_name
ORDER BY total_revenue DESC
LIMIT 6
```

#### Query D — Category performance
```sql
SELECT
    COALESCE(dc.category_name, '(ไม่ระบุ)')                        AS category,
    ROUND(SUM(ft.net_amount), 2)                                    AS total_revenue,
    COUNT(ft.txn_key)                                               AS total_transactions,
    ROUND(
        SUM(ft.net_amount)::NUMERIC
        / NULLIF(SUM(SUM(ft.net_amount)) OVER (), 0), 4)            AS revenue_contribution
FROM fact_transaction ft
LEFT JOIN dim_product dp ON ft.product_key = dp.product_key
LEFT JOIN dim_category dc ON dp.category_key = dc.category_key
WHERE ft.txn_date_key >= 20260101
GROUP BY dc.category_name
ORDER BY total_revenue DESC
```

---

### Step 2 — Compute derived KPIs from query results (in JS inside the widget)

From Query A (aggregate all months):
- `total_revenue` = SUM net_revenue across all months
- `total_transactions` = SUM total_transactions across all months
- `ytd_achievement` = total_revenue / (400000 × months_count) × 100
- `total_returns` = SUM returns across all months
- `avg_basket` = total_revenue / total_transactions

From Query B:
- `total_skus` from result
- `critical_count` = no_sales + critical (both require urgent action)
- `warning_count`, `watch_count`, `active_count`

---

### Step 3 — Render with `visualize:show_widget`

#### Layout (top to bottom, single column, full width)

```
┌─────────────────────────────────────────────────┐
│  Section label: "YTD 2026 — overview"           │
│  KPI strip (5 cards in one row):                │
│  Net Revenue | Transactions | Achievement % | Avg Basket | Returns │
├─────────────────────────────────────────────────┤
│  Section label: "Slow-moving product health"    │
│  4 colored cards (Critical | Warning | Watch | Active) │
├─────────────────────────────────────────────────┤
│  Section label: "Monthly revenue trend"         │
│  Full-width chart: grouped bar (transactions÷10, revenue÷10000) │
│  + line overlay (achievement %)  — height 220px │
├───────────────────────┬─────────────────────────┤
│ "Revenue by category" │ "Top staff — revenue"   │
│  Horizontal bar chart │  Horizontal bar chart   │
│  height 220px         │  height 220px           │
└───────────────────────┴─────────────────────────┘
```

#### KPI card rules
- 5 cards in `grid-template-columns: repeat(5, 1fr)`, `gap: 10px`
- background: `var(--color-background-secondary)`, `border-radius: var(--border-radius-md)`, padding `0.85rem 1rem`
- label: 12px, `var(--color-text-secondary)`; value: 22px, weight 500; sub-text: 11px
- Color coding:
  - Net Revenue → `var(--color-text-primary)`
  - Transactions → `var(--color-text-primary)`
  - Achievement % → red `#A32D2D` if < 80%, amber `#854F0B` if 80–99%, green `#3B6D11` if ≥ 100%
  - Avg Basket → `var(--color-text-primary)`
  - Returns → always amber `#854F0B`
- sub-text shows target: e.g. "target ≥100%" in red when missing

#### Slow-moving product health cards
- 4 cards: Critical (`#FCEBEB` bg, `#A32D2D` text) | Warning (`#FAEEDA`, `#854F0B`) | Watch (`#E6F1FB`, `#185FA5`) | Active (`#EAF3DE`, `#3B6D11`)
- Critical card shows `no_sales + critical` combined count (both require urgent action)
- Large number (26px), label (12px bold), sub-text (11px, muted) describing threshold

#### Monthly revenue trend chart (Chart.js)
- Type: `bar` with `line` overlay on second y-axis
- Bars: Transactions ÷ 10 (blue `#B5D4F4`) and Net revenue ÷ 10000 (green `#639922`) — grouped
- Line: Achievement % × 100 (red dashed `#E24B4A`, borderDash `[4,3]`)
- Two y-axes: left for scaled counts, right for % (label callback: `v + '%'`)
- x-axis labels: Thai month abbreviations `['ม.ค.','ก.พ.','มี.ค.','เม.ย.','พ.ค.','มิ.ย.',...]`
- `autoSkip: false` on x-axis ticks
- Custom HTML legend above chart; no Chart.js default legend

#### Category revenue chart (Chart.js horizontal bar)
- Sort by total_revenue descending
- Each bar a distinct color from: `['#639922','#378ADD','#1D9E75','#BA7517','#7F77DD','#E24B4A']`
- x-axis ticks: format as `฿{value/1000}K`
- Wrapper div height: `(number_of_bars × 40) + 80`px

#### Staff revenue chart (Chart.js horizontal bar)
- Top 6 staff, sorted by total_revenue DESC
- x-axis ticks: format as `฿{value/1000}K`
- Bar colors from green ramp: `['#639922','#3B6D11','#1D9E75','#378ADD','#185FA5','#0C447C']`
- Wrapper div height: `(6 × 40) + 80`px

#### Chart.js shared rules
- Load from: `https://cdnjs.cloudflare.com/ajax/libs/Chart.js/4.4.1/chart.umd.js`
- `responsive: true`, `maintainAspectRatio: false`
- `plugins: { legend: { display: false } }` — always use custom HTML legend
- Grid lines: `color: 'rgba(0,0,0,0.05)'` on value axis only
- Canvas must have `role="img"` and `aria-label`

#### Section labels
- 11px, weight 500, `var(--color-text-secondary)`, uppercase, `letter-spacing: 0.06em`
- margin: `1.5rem 0 0.6rem`

#### Chart wrapper cards (monthly + the 2-col row)
- `background: var(--color-background-primary)`, `border: 0.5px solid var(--color-border-tertiary)`, `border-radius: var(--border-radius-lg)`, `padding: 1rem 1.25rem`
- Chart title: 13px, weight 500

---

## KPI reference (for context only — not printed in any output)
| KPI                 | Target     |
|---------------------|------------|
| Monthly Net Revenue | ≥ ฿400,000 |
| Achievement %       | ≥ 100%     |
| Avg Basket Size     | ≥ ฿350     |
| Upsell Rate         | ≥ 15%      |
| Return Rate         | ≤ 5%       |

## Key schema facts (fictional retail database)
- `fact_transaction` is the primary fact table (like `fact_lead_header`)
- `txn_date_key` is a YYYYMMDD integer; month = `(txn_date_key / 100) % 100`
- `txn_status` encodes: COMPLETED / RETURN (like `lead_type` WIN/LOSS)
- `customer_type` encodes: NEW / RETURNING (like lead source classification)
- `has_upsell` is a boolean flag on each transaction
- `dp.margin_pct` is a stored margin % on `dim_product`
- MCP tool hard cap: 500 rows per call
- Key joins:
  - `fact_transaction.product_key = dim_product.product_key`
  - `fact_transaction.staff_key = dim_staff.staff_key`
  - `fact_transaction.customer_key = fact_customer.customer_key`
  - `dim_product.category_key = dim_category.category_key`
  - `dim_staff.branch_key = dim_branch.branch_key`