Welcome to the Retail Sales Analysis Project, where I explored a rich retail dataset using SQL to uncover valuable insights, identify business trends, and answer key questions. This project focuses on leveraging SQL queries to solve real-world business problems.
In this project, I focused on the following:
- Data Cleaning: Removed missing or irrelevant data to ensure the dataset's accuracy and reliability.
- Exploratory Data Analysis: Explored key metrics like total sales, unique customers, product categories, and customer demographics.
- Business Insight Generation: Answered business-critical questions to derive actionable insights, such as top-selling products, customer behavior, and sales trends.
- Advanced Query Techniques: Utilized complex SQL techniques such as window functions, CTEs, and aggregation to answer high-level business questions.
Here are the critical SQL queries used to solve business problems:
- Retrieve all columns for sales made on '2022-11-05'.
- Find transactions where the category is 'Clothing' and quantity sold is more than 10 in Nov 2022.
- Calculate total sales for each product category.
- Find the average age of customers who purchased items from the 'Beauty' category.
- Identify transactions where total sales exceed 1000.
- Analyze the total number of transactions (transaction IDs) by gender for each category.
- Calculate the average sales for each month and identify the best-selling month each year.
- Find the top 5 customers based on the highest total sales.
- Find the number of unique customers who purchased items from each category.
- Classify orders into shifts (Morning, Afternoon, Evening) based on sale time and count the number of orders in each shift.
- Sales Trends: Identified the highest-grossing months and products, helping businesses focus on peak times.
- Customer Insights: Pinpointed top customers and their purchasing patterns, aiding targeted marketing campaigns.
- Category Performance: Analyzed performance across different product categories and identified best sellers.
- Shift Analysis: Segmented sales based on time of day (Morning, Afternoon, Evening), providing insights into sales patterns throughout the day.
This analysis not only answered fundamental business questions but also provided a foundation for decision-making. Whether you are looking to optimize product categories, improve customer targeting, or gain insights into sales patterns, SQL-powered insights can unlock the potential for growth.
- Email: arundeepp9393@gmail.com
- LinkedIn: linkedin.com/in/arun
- GitHub: github.com/ArunCooksData
Let’s Cook Data Together and Serve Powerful Insights! 🍳📊