README.md

Title: Sales Analysis and Insights from Paper Orders Database

This project involves conducting a comprehensive sales analysis on a database containing records of paper orders from various companies like Walmart and Microsoft. The database schema includes tables for accounts (companies), orders, regions, sales representatives, and web events. The goal is to extract meaningful insights such as identifying best-selling products, top customers, and sales growth rates across different regions.

Project Tasks

1. Region and Sales Representatives: Provide a table listing sales representatives and their associated accounts in the Midwest region, sorted alphabetically by account name.
2. Filtered by Sales Rep Name: Provide a table listing sales representatives and their associated accounts in the Midwest region where the sales rep's first name starts with 'S', sorted alphabetically by account name.
3. Filtered by Sales Rep Last Name: Provide a table listing sales representatives and their associated accounts in the Midwest region where the sales rep's last name starts with 'K', sorted alphabetically by account name.
4. Unit Price Calculation: Provide a table showing the region, account name, and unit price paid for orders where the standard order quantity exceeds 100.
5. Sorting by Unit Price: Similar to Task 4, but include only orders where the standard order quantity exceeds 100 and the poster order quantity exceeds 50, sorted by unit price in ascending order.
6. Sorting by Largest Unit Price: Similar to Task 5, but sorted by unit price in descending order.
7. Average Paper Purchases: Calculate the average amount of each type of paper purchased (standard, gloss, poster) per order for each account.
8. Average Spending per Paper Type: Determine the average amount spent per order on each paper type (standard, gloss, poster) for each account.
9. Average Spending per Order: Similar to Task 8, but focus on the average amount spent per order on each paper type.
10. Web Events Analysis: Determine the number of occurrences for each channel in the web_events table for each sales representative, sorted by the highest number of occurrences.
11. Annual Sales Totals: Summarize annual sales totals for key revenue trends, sorted in ascending order.
12. Monthly Sales Totals: Summarize monthly sales totals for specific years, arranged by year to reveal revenue patterns.
13. Daily Sales Totals for 2017: Summarize daily sales totals for the year 2017 to analyze daily revenue trends.
14. Daily Sales Totals for January 1st: Summarize daily sales totals specifically for January 1st to understand the start of each year's sales trends.
15. Identifying Top Spending Month: Determine the month and year in which Walmart spent the most on gloss paper.

This project README aims to provide a structured overview of the project scope, database schema, and specific SQL queries used to derive insights. It serves as a guide for understanding and replicating the analysis process for sales data from the database.