Exploratory Retail Data Analysis

📊 Retail Data Analysis

Problem: Businesses struggle to understand sales performance and customer behavior from large transactional datasets.

Tools Used: SQL (MySQL)

Approach

• Imported and cleaned raw retail dataset using SQL

• Performed revenue, product, and customer analysis using aggregation queries

• Identified trends and patterns through exploratory analysis

Key Insights

• Top countries contributed the majority of revenue

• Monthly sales trends revealed seasonality and peak periods

• Best-selling products were identified, highlighting customer preferences

• High-value customers were identified, contributing significant revenue

Business Impact: This analysis helps businesses make data-driven decisions in sales strategy, customer targeting, and inventory planning.

Project Link:

• Link to Code: https://github.com/prt-tv/Priti-s-Porfolio/blob/main/Exploratory%20Openeretail%20%20Data%20Analysis.sql

First, let's take look at the data:

Revenue by Country

Displays the top 10 countries by total revenue, highlighting the most profitable markets.

Time-Based Analysis

SELECT DATE_FORMAT(InvoiceDate, '%Y-%m') AS Month, ROUND(SUM(Quantity * UnitPrice), 2) AS Revenue
FROM onlineretail
GROUP BY Month
ORDER BY Month;

Presents the monthly revenue trend, revealing sales patterns and seasonality over time.

Product Analysis

SELECT Description, SUM(Quantity) AS TotalSold
FROM onlineretail
GROUP BY Description
ORDER BY TotalSold DESC
LIMIT 10;

dentifies the top 10 best-selling products by quantity, uncovering key customer preferences.

Customer Behavior:

SELECT CustomerID, ROUND(SUM(UnitPrice), 2) AS TotalSpent
FROM onlineretail
GROUP BY CustomerID
ORDER BY TotalSpent DESC
LIMIT 10;

Retrieves the top 10 customers ranked by their total spending, highlighting key contributors to revenue.