Retail Sales Analytics Dashboard (2022–2023)

Project Overview

A full end-to-end Business & Sales Analytics project that simulates a real Egyptian retail company with 2,000 transactions over 2 years. The goal is to extract actionable insights from sales data using Python, SQL-style queries, and interactive visualizations.

🎯 Business Questions Answered

# Question

1 Which product category generates the most revenue?

2 What is the monthly revenue trend over 2 years?

3 Which region has the highest sales volume?

4 Who is the top-performing sales representative?

5 How does discount level affect average order value?

6 What is the most popular payment method?

7 What is the quarterly revenue trend per category?

8 How does customer satisfaction correlate with order value?

📊 Dashboard Preview

Overview Dashboard

Dashboard Overview

Deep-Dive Analysis

Deep Dive

📁 Project Structure

Retail-Sales-Analytics/

│

├── 📄 sales_data.csv # Raw dataset (2,000 transactions)

├── 🐍 generate_data.py # Data generation script

├── 🐍 analysis.py # Full EDA + SQL-style analysis

├── 📊 dashboard_overview.png # Main KPI dashboard

├── 📊 deep_dive_analysis.png # Correlation & trend analysis

├── 📋 analysis_summary.xlsx # Excel report (3 sheets)

└── 📖 README.md

📦 Dataset Description

Column Type Description

Order_ID String Unique order identifier

Date Date Transaction date (2022–2023)

Category String Product category (5 categories)

Region String Egyptian city

Sales_Rep String Representative name

Quantity Integer Units sold

Unit_Price Float Price per unit ($)

Discount Float Discount applied (0–20%)

Total_Sales Float Net revenue after discount

Payment_Method String Payment type

Customer_Age Integer Customer age

Customer_Satisfaction Integer Rating 1–5

🔍 Key Findings

💰 Electronics is the highest-revenue category (~30% of total sales)

📈 Revenue shows a positive growth trend in Q4 2023

🗺️ Cairo leads in total regional revenue

👤 Nour Ibrahim is the top sales representative ($2.06M)

💳 Credit Card is the most preferred payment method

🏷️ Orders with 10–15% discount show the highest average order value

😊 Customer satisfaction score doesn't strongly predict order size — suggesting pricing is needs-driven

🛠️ Technologies Used

pandas # Data manipulation & SQL-style queries

numpy # Numerical operations

matplotlib # Custom charts & dashboards

seaborn # Statistical visualizations

openpyxl # Excel report export