This project focuses on analyzing a retail sales dataset using PostgreSQL to generate key business insights. The goal is to extract meaningful KPIs that help evaluate revenue performance, customer behavior, product trends, and discount impact.
📊 Dataset Columns
-Transaction ID
-Customer ID
-Category
-Item
-Price Per Unit
-Quantity
-Total Spent
-Payment Method
-Location
-Transaction Date
-Discount Applied
-Total Revenue
-Total Transactions
-Average Order Value (AOV)
-Total Unique Customers
-Revenue Per Customer
-Top 5 Selling Items
-Revenue by Payment Method
-Revenue by Location
-Monthly Revenue Trend
-Revenue Without Discount
-PostgreSQL
-SQL (Aggregate Functions, GROUP BY, DATE_TRUNC, Casting)
-Identified top revenue-generating products
-Evaluated customer purchase behavior
-Analyzed impact of discounts on revenue
-Tracked monthly revenue trends
-Compared performance across payment methods and locations
This project demonstrates:
-Strong SQL fundamentals
-KPI-based business thinking
-Data aggregation and grouping skills
-Time-based analysis using SQL
-Create the retail_sales table in PostgreSQL
-Import the CSV file using COPY / \COPY
-Execute the SQL queries provided in the project