Retail Sales Analysis
A comprehensive data analytics project focused on identifying sales trends, regional performance, customer behavior, and profitability drivers to support data-driven retail decision-making.
📌 Problem Statement
Retail businesses generate large volumes of transactional data, but without
structured analysis, it becomes difficult to identify trends, high-performing
regions, and valuable customers.
This project aims to transform raw retail sales data into actionable insights
using data analytics and visualization techniques.
🎯 Objectives
- Analyze monthly and quarterly sales performance
- Identify high-revenue and high-profit regions
- Determine top customers contributing to revenue
- Understand payment behavior and sales trends
📊 Data Understanding
The dataset consists of retail transactional data containing:
- Order ID, Order Date, Ship Date
- Customer Name and Region
- Product Category and Sub-Category
- Sales Amount, Profit, Quantity
- Payment Mode information
🖼 Visualizations & Insights
Overall Sales Dashboard
Regional Sales Analysis
Himachal Analytics
Top 10 Customers
Sales Trends Over Time
Payment Category Distribution
💡 Key Insights
- Certain regions consistently outperform others in terms of sales and profit
- A small group of customers contributes a significant portion of revenue
- Sales show clear seasonal and monthly trends
- Digital payment methods dominate transaction volume
🧰 Technologies Used
Power BI / Tableau – Visualization
Python (Pandas, NumPy) – Data Analysis
SQL – Data Querying
Excel / CSV – Data Storage
🧩 Methodology
- Data collection and cleaning
- Exploratory Data Analysis (EDA)
- Feature engineering and aggregation
- Dashboard creation and interpretation
- Insight generation for business decisions
🚀 Future Scope
- Customer segmentation and clustering
- Sales forecasting using time-series models
- Inventory and demand optimization