Retail Sales SQL Analysis

SQL Server EDA Business Analysis

Problem Statement

Retail businesses generate large volumes of transactional data, but without structured analysis, it becomes difficult to extract meaningful insights.

This project focuses on analyzing sales performance, customer behavior, and operational trends using SQL to support data-driven decision making.

Dataset & Structure

The dataset consists of transactional retail data including customer demographics, product categories, and sales values.

        CREATE TABLE retail_sales (  
            transactions_id INT PRIMARY KEY, 
            sale_date DATE,  
            sale_time TIME,  
            customer_id INT,  
            gender VARCHAR(10),  
            age INT,  
            category VARCHAR(35),  
            quantity INT,  
            price_per_unit FLOAT,  
            cogs FLOAT,  
            total_sale FLOAT
        );
    

Data Cleaning

Ensured data quality by identifying and removing records with missing values across key fields.

Removed 3 records with NULL values to maintain data consistency and accuracy.

Key Analysis Performed

Sales Performance Analysis

Total transactions and revenue trends
Category-wise sales performance

Customer Analysis

Unique customer count
Top 5 customers by revenue
Customer demographics (age, gender)

Operational Insights

High-value transactions (>1000 sales)
Best-performing months using window functions
Sales distribution across time of day (Morning/Afternoon/Evening)

SQL Techniques Used

Aggregations (SUM, COUNT, AVG)
Filtering & conditional logic (WHERE, CASE)
Window functions (RANK)
Subqueries and CTEs
Date & time functions

Key Insights

Certain product categories consistently drive higher sales volume.

A small group of customers contributes disproportionately to total revenue.

Sales activity peaks during specific times of the day, indicating operational optimization opportunities.

Business Implications

The analysis provides a foundation for:

Customer targeting and retention strategies
Inventory and category optimization
Time-based staffing and operational planning

Future Enhancements

Build dashboards in Power BI
Add indexing for query optimization
Extend analysis to profitability using COGS

Retail Sales Analysis (SQL)