Retail Sales Analysis SQL Project

Project Overview

Project Title: Retail Sales Analysis
Database: retailsalesanalysis

This project is designed to demonstrate POSTGRES 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.

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 retailsalesanalysis.
• Table Creation: A table named retailsales 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.
• Importing csv file to pgadmin(postgres): Select the table and right click on it and click on Import/Export data and select the path of the csv file and submit.

CREATE DATABASE retailsalesanalysis;

CREATE TABLE retailsales
(
    transactions_id INT PRIMARY KEY,
    sale_date DATE,	
    sale_time TIME,
    customer_id INT,	
    gender VARCHAR(10),
    age INT,
    category VARCHAR(20),
    quantity 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.

SELECT COUNT(*) FROM retailsales;
SELECT COUNT(DISTINCT customer_id) FROM retailsales;
SELECT DISTINCT category FROM retailsales;

SELECT * FROM retailsales
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
    quantiy IS NULL
    OR
    price_per_unit IS NULL
    OR
    cogs IS NULL
    OR
    total_sale IS NULL;

DELETE FROM retailsales
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
    quantiy 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 retailsales
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 3 in the month of Nov-2022:

SELECT *  FROM retailsales
WHERE category = 'Clothing' AND TO_CHAR(sale_date, 'YYYY-MM') = '2022-11' AND quantiy >= 3;

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

SELECT category, SUM(total_sale) netsale, COUNT(total_sale) totalOrder FROM retailsales    
GROUP BY category;

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

SELECT category, ROUND(AVG(AGE), 2) avg_age FROM retailsales
WHERE category = 'Beauty'
GROUP BY category;

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

SELECT * FROM retailsales
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(transactions_id) FROM retailsales
GROUP BY category, gender
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 * FROM (
	SELECT 
		EXTRACT(year FROM sale_date) AS year, 
		EXTRACT(month FROM sale_date) AS month, 
		AVG(total_sale) AS avg_sale,
		RANK() OVER(PARTITION BY EXTRACT(year FROM sale_date) ORDER BY AVG(total_sale) DESC) AS rank
	FROM retailsales
	GROUP BY 1, 2
) AS t1
WHERE rank = 1

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

SELECT customer_id, SUM(total_sale) AS total_sale FROM retailsales
GROUP BY 1
ORDER BY 2 DESC
LIMIT 5;

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

SELECT category, COUNT(DISTINCT customer_id) uniquecustomer  FROM retailsales
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 hourly_sales AS(
	SELECT *,
		CASE
			WHEN EXTRACT(Hour FROM sale_time) < 12  THEN 'Morning'
			WHEN EXTRACT(Hour FROM sale_time) BETWEEN 12 AND 17 THEN 'Afternoon'
			ELSE 'Evening'
		END AS shift
	FROM retailsales
) 
SELECT shift, COUNT(*) AS total_orders FROM hourly_Sales
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 guide 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 Creating_Database.sql file to create and populate the database.
3. Run the Queries: Use the SQL queries provided in the DataExploration_and_cleaning.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 - Anoop George

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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Thank you for your support, and I look forward to connecting with you!