Sales Performance Analysis

About the Project

This project analyzes sales performance, customer behavior, product trends, and operational logistics using SQL. The dataset contains sales transactions from a fictional retail superstore, allowing us to extract valuable insights into revenue trends, customer segmentation, product demand, and shipping efficiency.

Dataset Overview

The Superstore dataset consists of 9,999 rows and 21 columns, representing transactional data. It has been cleaned and structured into four relational tables:

• Customers: Contains customer details such as ID, name, and market segment.
• Orders: Tracks order placement details, dates, shipping, and location info.
• Products: Lists product details, categories, and subcategories.
• Sales: Records transactions, including revenue, quantity, discount, and profit.

Entity Relationship Diagram (ERD)

To understand the relationships between these tables, refer to the Entity Relationship Diagram (ERD):

Data Cleaning Process

Performed in Jupyter Notebook using Python (pandas, NumPy) to ensure data consistency before database creation.

• Removed duplicates & handled missing values
• Converted dates to proper format (order_date, ship_date)
• Standardized categorical values
• Split data into four normalized tables
  Notebook Location: data_cleaning.ipynb

Key Insights & Analysis

1. Sales Performance & Revenue: Analyzed total sales, profit trends, and discount impact. Identified growth patterns across months, quarters, and years to optimize pricing and promotions.

2. Customer Behavior & Segmentation: Classified customers into high, medium, and low-value groups. Conducted RFM analysis to track purchasing habits, retention, and churn rates.

3. Product Performance & Market Insights: Identified best and worst-selling products, analyzed category contributions, and performed market basket analysis to uncover frequently bought-together items.

4. Operational & Logistics Efficiency: Evaluated shipping times, delays, and cost-effectiveness across shipping modes. Assessed seasonality trends to optimize logistics and delivery performance.

Technologies Used

• SQL (MySQL) for data analysis
• CTEs & Temporary Tables for efficient queries
• Window Functions (DENSE_RANK, LAG, NTILE)
• Aggregation & Joins for relational insights
• Python (pandas) for data cleaning

Project Structure

Superstore-Data-Analytics
│── 📁 data/               # Contains dataset files
│    ├── README.md         # Detailed dataset description
│    ├── Superstore.csv    # Raw dataset
│
│── 📁 data_cleaning/          
│    ├── data_cleaning.ipynb   # Jupyter notebooks for data cleaning
│    ├── erd_diagram.png       # ERD diagram  
│
│── 📁 queries/              # SQL scripts for analysis
│    ├── superstore_db.sql    # Creates database & tables
│    ├── analysis.sql      # SQL queries for business insights
│
│── README.md              # Project documentation (this file)