Retail Sales Analysis SQL Project

Project Overview

Project Title: Retail Sales Analysis
Level: Beginner
Database: sql_project1

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 sql_project1.
• 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.

--  CREATING 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(8),
    age	int(2),
    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.

select * from retail_sales limit 10;
select count(*) from retail_sales;
select * from retail_sales limit 500;


 -- CHECKING NULL VALUES - DATA CLEANING 
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 
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:

-- DATA EXPLORATION
--  How many sales do we have? 
SELECT COUNT(TRANSACTIONS_ID) AS TOTAL_SALES FROM RETAIL_SALES;

-- How many unique customers do we have?
select count(distinct customer_id) from retail_sales;

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 quantiy >= 4 and date_format(sale_date, '%Y-%m') = '2022-11';

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

SELECT category,sum(total_sale) as net_sale, count(*) as total_orders 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 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(transactions_id) from retail_sales group by category,gender order by category asc;

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

SELECT Year(sale_date) as Year,Month(sale_date) as Month, avg(total_sale) as Average_Sale from retail_sales group by Year,Month;

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_sales FROM retail_sales GROUP BY customer_id ORDER BY total_sales 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)) as No_of_customers 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):

SELECT 
    CASE
        WHEN HOUR(sale_time) < 12 THEN 'Morning'
        WHEN HOUR(sale_time) BETWEEN 12 AND 17 THEN 'Afternoon'
        ELSE 'Evening'
    END AS shift,
     COUNT(transactions_id) AS number_of_orders
FROM retail_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 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 SQL - Retail Sales Analysis_utf.csv file to create and populate the database.
3. Run the Queries: Use the SQL queries provided in the sql_query_p1.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 - Sahil Shukre

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!

Thank you and I look forward to connecting with you!