End-to-end data analysis project using SQL, Excel & Power BI
📌 1. Project Problem Statement
The goal of this project is to analyze Superstore sales data to understand:
● Which products, categories and regions drive revenue and profit
● Which products show high sales but low profit due to heavy discounts
● Customer purchasing trends and top buyers
● Shipping performance and delivery timelines
● Impact of discount on profitability
● Monthly sales trends for forecasting
The final output includes SQL analysis, Power BI dashboards and a documentation file summarizing findings.
📁 2. About the Dataset
● Dataset Source: Kaggle — Superstore Dataset (Users need to download separately)
● Rows: ~9K+
● Time Range: 2014–2017
● Data Includes:
🔹Order details
🔹Product categories
🔹Sales, profit, quantity
🔹Customer information
🔹Shipping time
🔹Discounts
🔹Regional & geographic data
The dataset was cleaned and formatted in Excel, analyzed using SQL (MySQL Workbench) and visualized using Power BI.
The CSV file is not included in this repository due to licensing restrictions. Users can download it directly from Kaggle using the link above.
🛠 3. Tools & Technologies Used
Tool --> Purpose
🔹MySQL Workbench --> SQL queries, data analysis
🔹Microsoft Excel --> Query results, Data cleaning, formatting
🔹Power BI --> Dashboard creation, data visualization
🔹GitHub --> Version control and project hosting
🧮 4. SQL Queries Summary
A complete SQL file (Queries.sql) contains all the queries used in this project, including:
🔹 KPIs & Metrics
● Total Sales
● Total Profit
● Total Quantity
● Average Discount
● Number of Customers
🔹 Business Insights Queries
● Top 10 products by profit
● Top 10 products by sales
● Top categories driving revenue and profit
● Sales by region/state
● Best performing customer
● Shipping mode performance
● Monthly sales trend
🔹 Advanced Analysis
● Identify products with high sales but low profit due to high discount using statistical thresholds (AVG + STDDEV)
● Discount impact analysis using grouped profit averages
📊 5. Power BI Dashboard Overview
The dashboard provides the following visuals:
📌 High-Level KPIs
● Total Sales
● Total Profit
● Total Orders
● Total Customers
● Average Discount
📌 Product Insights
● Top-selling products
● Top profitable products
● High sales but low profit products (due to high discount)
📌 Trend Analysis
● Monthly sales trend (2014–2017)
📌 Customer Insights
● Top 10 high-value customers
● Customer segments
📌 Shipping Analysis
● Shipping mode performance
● Avg shipping time per mode
📌 Regional Insights
● US map showing sales distribution
● State-level performance
🔍 6. Insights (Based on SQL Queries + Dashboard)
✅ 1. Key KPIs
● Strong overall sales and profit figures
● High discount averages in some categories reduce profitability
✅ 2. Top Products & Categories
● Technology and Office Supplies generate the highest revenue
● Furniture shows lower profitability due to discounts
✅ 3. High Sales + Low Profit Products
● Several products generate high sales but very low margins, directly linked to higher discount rates
● These items should be reviewed for pricing & discount strategy
✅ 4. Regional Sales
● California leads in sales
● West region performs consistently
● Some states show minimal contribution, potential for targeted marketing
✅ 5. Discount Impact on Profit
● Profit consistently decreases as discount increases
● Heavy discounting dramatically reduces margins
✅ 6. Customer Insights
● A small portion of customers contributes a major share of revenue
● Top 10 best customers identified through sales aggregation
✅ 7. Shipping Performance
● Some shipping modes take longer delivery times
● Standard Class has the highest shipment count
✅ 8. Seasonal trends observed
● Sales peak strongly during Q4 (Nov–Dec)
🔹 Clone the repository
🔹 Download the dataset from Kaggle and save it as Sample - Superstores.csv
🔹 Load Sample - Superstores.csv into MySQL
🔹 Run SQL queries from Queries.sql
🔹 Open the .pbix file in Power BI
🔹 Refresh data connection
🔹 Interact with the dashboard filters
🔹 Open the PDF for query outputs and screenshots
🏁 9. Conclusion
This project demonstrates skills in:
✔ Data cleaning
✔ Statistical analysis
✔ Business insights
✔ SQL analytics
✔ Power BI dashboarding
https://github.com/CoreSyntax10/superstore-sales-analysis/blob/main/dashboard_view_1.PNG
https://github.com/CoreSyntax10/superstore-sales-analysis/blob/main/dashboard_view_2.PNG
https://github.com/CoreSyntax10/superstore-sales-analysis/blob/main/dashboard_view_3.PNG
