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Retail Reporting Templates
50+ retail report templates: daily sales, inventory turnover, sell-through rates, vendor performance, and seasonal analysis.
MarkdownPythonSQLPostgreSQLAirflow
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retail-reporting-templates/
├── LICENSE
├── README.md
├── docs/
│ └── metrics_definitions.md
├── free-sample.zip
├── guide/
│ ├── 01-overview.md
│ ├── 02-daily-sales-and-performance-reports.md
│ └── 03-inventory-and-vendor-analytics.md
├── guides/
│ └── reporting-guide.md
├── index.html
├── reports/
│ ├── daily_sales_sample.csv
│ ├── inventory_aging_sample.csv
│ └── margin_analysis_sample.csv
├── sql/
│ ├── finance/
│ │ ├── basket_analysis.sql
│ │ ├── breakeven.sql
│ │ ├── cash_flow.sql
│ │ ├── cogs.sql
│ │ ├── gross_margin.sql
│ │ ├── operating_expense.sql
│ │ ├── price_elasticity.sql
│ │ ├── profit_by_category.sql
│ │ ├── refund_impact.sql
│ │ ├── revenue_by_store.sql
│ │ ├── shipping_cost.sql
│ │ └── tax_collection.sql
│ ├── inventory/
│ │ ├── abc_classification.sql
│ │ ├── aging.sql
│ │ ├── dead_stock.sql
│ │ ├── reorder_point.sql
│ │ ├── sell_through_rate.sql
│ │ ├── shrinkage.sql
│ │ ├── stock_to_sales.sql
│ │ ├── turnover.sql
│ │ ├── valuation.sql
│ │ └── warehouse_utilization.sql
│ ├── sales/
│ │ ├── avg_order_value.sql
│ │ ├── by_category.sql
│ │ ├── by_channel.sql
│ │ ├── by_payment_method.sql
│ │ ├── customer_acquisition.sql
│ │ ├── daily_sales_summary.sql
│ │ ├── discount_impact.sql
│ │ ├── forecast_baseline.sql
│ │ ├── growth_comparison.sql
│ │ ├── hourly_breakdown.sql
│ │ ├── return_rate.sql
│ │ └── top_products.sql
│ ├── schema.sql
│ ├── seasonal/
│ │ ├── demand_pattern.sql
│ │ ├── event_impact.sql
│ │ ├── holiday_performance.sql
│ │ ├── inventory_planning.sql
│ │ ├── monthly_trend.sql
│ │ ├── peak_hour.sql
│ │ ├── promo_calendar.sql
│ │ ├── weather_correlation.sql
│ │ ├── weekly_patterns.sql
│ │ └── yoy_comparison.sql
│ └── vendor/
│ ├── cost_comparison.sql
│ ├── fill_rate.sql
│ ├── lead_time.sql
│ ├── payment_aging.sql
│ ├── performance_scorecard.sql
│ ├── po_status.sql
│ ├── quality_metrics.sql
│ └── return_rate.sql
└── src/
├── __init__.py
└── report_runner.py
📖 Documentation Preview README excerpt
Retail Reporting Templates
52 ready-to-use SQL report templates covering every aspect of retail analytics. Each query is documented with its purpose, required parameters, expected output columns, and business context. Plug them into your database, adjust the table names if needed, and start generating insights immediately.
What's Included
52 SQL report templates organized into five categories:
| Category | Count | Focus |
|---|---|---|
| Sales | 12 | Revenue, order trends, product performance, channel analysis |
| Inventory | 10 | Stock levels, turnover, aging, ABC classification |
| Vendor | 8 | Supplier performance, lead times, fill rates, cost tracking |
| Seasonal | 10 | Demand patterns, YoY comparisons, holiday analytics |
| Finance | 12 | Margins, profitability, COGS, cash flow, basket analysis |
Plus:
- Base retail database schema (
sql/schema.sql) - Python report runner for executing any template against your database
- Metrics definitions reference with formulas and interpretation guidance
- CSV output templates showing expected report formats
Quick Start
1. Set Up the Schema
The included schema defines the tables these reports query against. If you already have a retail database, use it as a reference to map your table names.
psql -d your_database -f sql/schema.sql
2. Run a Report
Pick any SQL file, review the header comments for required parameters, and execute:
# Direct execution with psql
psql -d your_database -v report_date="'2025-06-01'" -f sql/sales/daily_sales_summary.sql
# Or use the Python runner
python src/report_runner.py --report sales/daily_sales_summary --date 2025-06-01
3. Browse by Category
sql/
├── schema.sql # Base retail database schema
├── sales/ # Revenue and order analytics
│ ├── daily_sales_summary.sql
│ ├── hourly_breakdown.sql
│ ├── by_category.sql
│ ├── by_channel.sql
│ ├── top_products.sql
│ ├── growth_comparison.sql
│ ├── avg_order_value.sql
│ ├── by_payment_method.sql
│ ├── discount_impact.sql
│ ├── return_rate.sql
*... continues with setup instructions, usage examples, and more.*
📄 Code Sample .py preview
src/report_runner.py
"""
Report runner for executing SQL templates against a database.
Loads SQL template files, substitutes parameters, executes
against a PostgreSQL database, and exports results to CSV.
Designed as a lightweight CLI tool -- no ORM, no framework.
Usage:
python src/report_runner.py --report sales/daily_sales_summary \\
--date 2025-06-01 --output output/
python src/report_runner.py --report inventory/aging \\
--start-date 2025-01-01 --end-date 2025-06-30
python src/report_runner.py --list
"""
from __future__ import annotations
import argparse
import csv
import logging
import os
import re
import sys
from datetime import date, datetime, timedelta
from pathlib import Path
from typing import Any, Optional
logger = logging.getLogger(__name__)
# Where SQL templates live, relative to this file
SQL_DIR = Path(__file__).resolve().parent.parent / "sql"
class ReportRunner:
"""Executes SQL report templates and exports results.
This runner works in two modes:
1. Connected mode: pass a database connection for live execution
2. Preview mode: loads and prepares SQL without executing
"""
def __init__(
self,
host: str = "localhost",
port: int = 5432,
database: str = "retail_db",
user: str = "analyst",
password: str = "",
# ... 331 more lines ...