sales-report

name	sales_report
description	Generate a comprehensive sales report by store or by state. Use this skill when the user asks for a sales report, performance summary, store report, state report, executive summary, or full analysis of a specific store or state.

Sales Report Generator

Generate a structured executive sales report with top products, trends, missed opportunities, and recommended actions.

When to Use

• User asks for a "sales report" or "performance report"
• User asks for analysis of a specific store or state
• User asks for an "executive summary" or "full breakdown"
• User mentions "report for [store name]" or "report for [state]"

Instructions

Step 1: Determine Report Scope

Extract from the user's question:

• Report Type: STORE (single store) or STATE (single state)
• Filter Value: The specific store name or state code

If the user says "by store" without naming one, ask which store. If they say "by state" without naming one, ask which state.

Step 2: Run Report Queries

Execute the following queries sequentially, substituting the appropriate filter.

For STORE reports, use: WHERE s.STORE_NAME = '{STORE_NAME}' For STATE reports, use: WHERE s.STATE = '{STATE_CODE}'

Query 1: Summary Metrics

SELECT
    COUNT(DISTINCT f.SALE_ID) AS total_transactions,
    ROUND(SUM(f.TOTAL_SALES), 2) AS total_revenue,
    ROUND(AVG(f.TOTAL_SALES), 2) AS avg_transaction_value,
    SUM(f.QUANTITY_SOLD) AS total_units_sold,
    COUNT(DISTINCT f.SALE_DATE) AS active_days,
    ROUND(AVG(f.DISCOUNT_PCT), 1) AS avg_discount_pct
FROM HOL_COCO_COWORK.DATA.FACT_ITEM_SALES f
JOIN HOL_COCO_COWORK.DATA.DIM_STORE s ON f.STORE_ID = s.STORE_ID
{WHERE_CLAUSE};

Query 2: Top 10 Products by Revenue

SELECT
    i.ITEM_NAME,
    i.CATEGORY,
    SUM(f.TOTAL_SALES) AS revenue,
    SUM(f.QUANTITY_SOLD) AS units_sold,
    ROUND(AVG(f.DISCOUNT_PCT), 1) AS avg_discount
FROM HOL_COCO_COWORK.DATA.FACT_ITEM_SALES f
JOIN HOL_COCO_COWORK.DATA.DIM_STORE s ON f.STORE_ID = s.STORE_ID
JOIN HOL_COCO_COWORK.DATA.DIM_ITEM i ON f.ITEM_ID = i.ITEM_ID
{WHERE_CLAUSE}
GROUP BY i.ITEM_NAME, i.CATEGORY
ORDER BY revenue DESC
LIMIT 10;

Query 3: Monthly Revenue Trend

SELECT
    DATE_TRUNC('MONTH', f.SALE_DATE) AS month,
    ROUND(SUM(f.TOTAL_SALES), 2) AS revenue,
    SUM(f.QUANTITY_SOLD) AS units,
    COUNT(DISTINCT f.SALE_ID) AS transactions
FROM HOL_COCO_COWORK.DATA.FACT_ITEM_SALES f
JOIN HOL_COCO_COWORK.DATA.DIM_STORE s ON f.STORE_ID = s.STORE_ID
{WHERE_CLAUSE}
GROUP BY month
ORDER BY month;

Query 4: Category Performance (for missed opportunities)

SELECT
    i.CATEGORY,
    ROUND(SUM(f.TOTAL_SALES), 2) AS revenue,
    SUM(f.QUANTITY_SOLD) AS units_sold,
    ROUND(AVG(f.DISCOUNT_PCT), 1) AS avg_discount,
    COUNT(DISTINCT i.ITEM_ID) AS unique_items_sold
FROM HOL_COCO_COWORK.DATA.FACT_ITEM_SALES f
JOIN HOL_COCO_COWORK.DATA.DIM_STORE s ON f.STORE_ID = s.STORE_ID
JOIN HOL_COCO_COWORK.DATA.DIM_ITEM i ON f.ITEM_ID = i.ITEM_ID
{WHERE_CLAUSE}
GROUP BY i.CATEGORY
ORDER BY revenue DESC;

Query 5: Underperforming Categories (compare to chain average)

WITH store_cat AS (
    SELECT i.CATEGORY, ROUND(SUM(f.TOTAL_SALES), 2) AS local_revenue
    FROM HOL_COCO_COWORK.DATA.FACT_ITEM_SALES f
    JOIN HOL_COCO_COWORK.DATA.DIM_STORE s ON f.STORE_ID = s.STORE_ID
    JOIN HOL_COCO_COWORK.DATA.DIM_ITEM i ON f.ITEM_ID = i.ITEM_ID
    {WHERE_CLAUSE}
    GROUP BY i.CATEGORY
),
chain_avg AS (
    SELECT i.CATEGORY, ROUND(AVG(store_revenue), 2) AS avg_revenue
    FROM (
        SELECT s.STORE_ID, i.CATEGORY, SUM(f.TOTAL_SALES) AS store_revenue
        FROM HOL_COCO_COWORK.DATA.FACT_ITEM_SALES f
        JOIN HOL_COCO_COWORK.DATA.DIM_STORE s ON f.STORE_ID = s.STORE_ID
        JOIN HOL_COCO_COWORK.DATA.DIM_ITEM i ON f.ITEM_ID = i.ITEM_ID
        GROUP BY s.STORE_ID, i.CATEGORY
    ) sub
    GROUP BY i.CATEGORY
)
SELECT
    c.CATEGORY,
    COALESCE(sc.local_revenue, 0) AS local_revenue,
    c.avg_revenue AS chain_avg_revenue,
    ROUND(((COALESCE(sc.local_revenue, 0) - c.avg_revenue) / NULLIF(c.avg_revenue, 0))  , )  pct_vs_avg
 chain_avg c
  store_cat sc  c.CATEGORY  sc.CATEGORY
  pct_vs_avg 
LIMIT ;

Step 3: Compile and Present Report

Structure the output as a formatted executive report:

# Sales Report: {Store Name / State}
## Period: January - March 2025

---

## Executive Summary
- Total Revenue: ${total_revenue}
- Total Transactions: {total_transactions}
- Average Transaction Value: ${avg_transaction_value}
- Total Units Sold: {total_units_sold}
- Average Discount: {avg_discount_pct}%

---

## Top 10 Products by Revenue
[Table: Item Name | Category | Revenue | Units Sold | Avg Discount]

---

## Monthly Trend
[Table: Month | Revenue | Units | Transactions]
[Insight: Which month was strongest/weakest and % change]

---

## Missed Opportunities
[Categories where this store/state underperforms vs chain average]
- {Category}: {pct_vs_avg}% below chain average (${gap} in unrealized revenue)

---

## Recommended Actions
Based on the data:
1. [Action based on underperforming categories]
2. [Action based on discount patterns]
3. [Action based on trend direction]

Guidelines for Missed Opportunities

• A category is a "missed opportunity" if its revenue is >20% below the chain average
• Quantify the gap in dollars: chain_avg_revenue - local_revenue
• Focus on categories with high chain averages (large addressable opportunity)

Guidelines for Recommended Actions

Generate 3 actionable recommendations based on:

1. Product mix: If top categories here differ from chain average, suggest stocking adjustments
2. Discount strategy: If avg discount is significantly higher/lower than chain, suggest optimization
3. Trend momentum: If revenue is declining month-over-month, suggest promotional activity; if growing, suggest doubling down