E-commerce Sales Analysis Dashboard

Project Overview

This project analyzes an e-commerce dataset to identify key business insights related to revenue, sales performance, profitability, customer behavior, and regional trends. The project workflow:

• Data cleaning (Python)
• Data analysis (SQL using DuckDB)
• Data visualization (Power BI)

Tools & Technologies

• Python (pandas)
• DuckDB (SQL)/DuckDB was used as a local analytical database to simulate modern data warehouse workflows
• Power BI
• GitHub&Git

Dataset

Superstore dataset including:

• Orders
• Sales
• Profit
• Customers
• Categories
• Regions
• Shipping modes

Data Preparation

Performed data cleaning using Python:

• Removed duplicates
• Handled missing values
• Standardized column names
• Converted data types

Key Analysis

• Sales by Category Technology is the highest-performing category in terms of sales
• Profit vs Sales Furniture and Office Supplies generate sales but lower profit margins
• Top Products Top-selling products are concentrated in the Technology category
• Regional Performance West and East regions outperform others in total sales
• Customer Analysis A small group of customers contributes a large portion of revenue
• Time Trend Sales show an overall upward trend over time

Dashboard Features

• KPIs:
  • Total Revenue
  • Total Profit
  • Total Orders
• Interactive filters (Region, Segment, Category)
• Top N analysis (Top 10 customers)
• Sales trend over time (monthly/yearly)
• Category and regional performance analysis

Key Insights

• Technology is the highest-performing category in terms of sales
• Furniture and Office Supplies show lower profitability despite strong sales
• West and East regions outperform others in total sales
• A small group of customers contributes a large portion of revenue
• Sales show an overall upward trend over time

Business Recommendations

• Focus on high-performing categories such as Technology
• Improve profit margins in Furniture and Office Supplies
• Strengthen operations in high-performing regions
• Implement loyalty strategies for top customers

Project Structure

1. dashboard image
2. exported CSV files
3. E-commerce_Sales_duckdb.pbix
4. README.md
5. Superstore.csv
6. project.py

Dashboard Preview

Author:

Mouna Al-Nasser Data Analyst| BI Analyst