Retail Sales Analysis SQL Project

Project Overview

Project Title: Retail Sales Analysis
Level: Beginner
Database: Retail_Sales_db

This project is designed to demonstrate SQL skills and techniques typically used by data analysts to explore, clean, and analyze retail sales data. The project involves setting up a retail sales database, performing exploratory data analysis (EDA), and answering specific business questions through SQL queries. This project is ideal for those who are starting their journey in data analysis and want to build a solid foundation in SQL.

Objectives

1. Set up a retail sales database: Create and populate a retail sales database with the provided sales data.
2. Data Cleaning: Identify and remove any records with missing or null values.
3. Exploratory Data Analysis (EDA): Perform basic exploratory data analysis to understand the dataset.
4. Business Analysis: Use SQL to answer specific business questions and derive insights from the sales data.

Project Structure

1. Database Setup

• Database Creation: The project starts by creating a database named p1_retail_db.
• Table Creation: A table named retail_sales is created to store the sales data. The table structure includes columns for transaction ID, sale date, sale time, customer ID, gender, age, product category, quantity sold, price per unit, cost of goods sold (COGS), and total sale amount.

-- CREATE DATABASE
CREATE DATABASE Retail_Sales_db;

-- CREATE TABLE
DROP TABLE IF EXISTS Retail_Sales
CREATE TABLE Retail_Sales
			(
				transactions_id INT PRIMARY KEY,
				sale_date DATE,
				sale_time TIME,
				customer_id INT,
				gender VARCHAR(15),
				age INT,
				category VARCHAR(15)
				quantiy INT,
				price_per_unit FLOAT,
				cogs FLOAT,
				total_sale FLOAT
			)

2. Data Exploration & Cleaning

• Record Count: Determine the total number of records in the dataset.
• Customer Count: Find out how many unique customers are in the dataset.
• Category Count: Identify all unique product categories in the dataset.
• Null Value Check: Check for any null values in the dataset and delete records with missing data.

-- How many sales do we have?
SELECT COUNT(*) as total_sales FROM Retail_Sales;

-- How many unique customers do we have?
SELECT COUNT(DISTINCT customer_id) unique_cust FROM Retail_Sales;

-- How many unique categories do we have?
SELECT COUNT(DISTINCT category) unique_categ FROM Retail_Sales;

-- Checking for Nulls:
SELECT * FROM Retail_Sales
WHERE 
	transactions_id IS NULL
	or
	sale_date IS NULL
	or 
	sale_time IS NULL
	or 
	customer_id IS NULL
	or 
	gender IS NULL
	or 
	age IS NULL
	or 
	category IS NULL
	or 
	quantity IS NULL
	or
	price_per_unit IS NULL
	or
	cogs IS NULL
	or
	total_sale IS NULL;

-- DELETE These null records from the table:
DELETE FROM Retail_Sales
WHERE 
	transactions_id IS NULL
	or
	sale_date IS NULL
	or 
	sale_time IS NULL
	or 
	customer_id IS NULL
	or 
	gender IS NULL
	or 
	age IS NULL
	or 
	category IS NULL
	or 
	quantity IS NULL
	or
	price_per_unit IS NULL
	or
	cogs IS NULL
	or
	total_sale IS NULL;

3. Data Analysis & Findings

The following SQL queries were developed to answer specific business questions:

1. Write a SQL query to retrieve all columns for sales made on '2022-11-05:

SELECT *
FROM Retail_Sales
WHERE sale_date = '2022-11-05';

2. Write a SQL query to retrieve all transactions where the category is 'Clothing' and the quantity sold is more than 4 in the month of Nov-2022:

SELECT *
FROM Retail_Sales
WHERE 
	category = 'Clothing'
	AND sale_date >= '2022-11-01' 
	AND sale_date < '2022-12-01'
	AND quantity >= 4;

3. Write a SQL query to calculate the total sales (total_sale) and total orders for each category.:

SELECT 
	category,
	SUM(total_sale) As sales_by_category,
	COUNT(*) AS orders_by_category
FROM Retail_Sales
GROUP BY category;

4. Write a SQL query to find the average age of customers who purchased items from the 'Beauty' category.:

SELECT 
	'Beauty' AS category,
	AVG(age) AS avg_age
FROM Retail_Sales
WHERE category = 'Beauty';

5. Write a SQL query to find all transactions where the total_sale is greater than 1000.:

SELECT * FROM Retail_Sales
WHERE total_sale > 1000;

6. Write a SQL query to find the total number of transactions (transaction_id) made by each gender in each category.:

SELECT
	category,
	gender,
	COUNT(*) as total_transaction
FROM Retail_Sales
GROUP BY gender, category
ORDER BY category;

7. Write a SQL query to calculate the average sale for each month. Find out best selling month in each year:

SELECT
	year,
	month,
	avg_sale_by_month
FROM
(
	SELECT
		DATEPART(YEAR, sale_date) AS year,
		DATEPART(MONTH, sale_date) AS month,
		AVG(total_sale) AS avg_sale_by_month,
		RANK() OVER(PARTITION BY DATEPART(YEAR, sale_date) ORDER BY AVG(total_sale) DESC) as rank
	FROM Retail_Sales
	GROUP BY DATEPART(YEAR, sale_date), 
			 DATEPART(MONTH, sale_date)
) as t1
WHERE rank = 1

8. **Write a SQL query to find the top 5 customers based on the highest total sales **:

SELECT TOP 5
	customer_id,
	SUM(total_sale) AS total_sales_by_customer
FROM Retail_Sales
GROUP BY customer_id 
ORDER BY total_sales_by_customer DESC;

9. Write a SQL query to find the number of unique customers who purchased items from each category.:

SELECT 
	category,
	COUNT(DISTINCT customer_id) AS unqe_custmr_by_ctgry
FROM Retail_Sales
GROUP BY category;

10. Write a SQL query to create each shift and number of orders (Example Morning <12, Afternoon Between 12 & 17, Evening >17):

WITH shiftwise_sale
AS
(
SELECT *,
	CASE 
		WHEN DATEPART(Hour, sale_time) < 12 THEN 'Morning'
		WHEN DATEPART(Hour, sale_time) BETWEEN 12 and 17 THEN 'Afternoon'
		ELSE 'Evening'
	END as Shift
FROM Retail_Sales
)
SELECT 
	Shift,
	COUNT(*) AS total_orders
FROM shiftwise_sale
GROUP BY Shift;

Findings

• Customer Demographics: The dataset includes customers from various age groups, with sales distributed across different categories such as Clothing and Beauty.
• High-Value Transactions: Several transactions had a total sale amount greater than 1000, indicating premium purchases.
• Sales Trends: Monthly analysis shows variations in sales, helping identify peak seasons.
• Customer Insights: The analysis identifies the top-spending customers and the most popular product categories.

Reports

• Sales Summary: A detailed report summarizing total sales, customer demographics, and category performance.
• Trend Analysis: Insights into sales trends across different months and shifts.
• Customer Insights: Reports on top customers and unique customer counts per category.

Conclusion

This project serves as a comprehensive introduction to SQL for data analysts, covering database setup, data cleaning, exploratory data analysis, and business-driven SQL queries. The findings from this project can help drive business decisions by understanding sales patterns, customer behavior, and product performance.

How to Use

1. Clone the Repository: Clone this project repository from GitHub.
2. Set Up the Database: Run the SQL scripts provided in the SQLQuery1 - Database Creation.sql file to create and populate the database.
3. Run the Queries: Use the SQL queries provided in the SQLQuery3 - Data Exploration and Analysis.sql file to perform your analysis.
4. Explore and Modify: Feel free to modify the queries to explore different aspects of the dataset or answer additional business questions.

Author - UnbeatenAnalytics

This project is part of my portfolio, showcasing the SQL skills essential for data analyst roles. If you have any questions, feedback, or would like to collaborate, feel free to get in touch!

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