This project analyzes the Sample Superstore dataset to understand sales performance, profitability, customer behavior, product performance, regional trends, and changes over time.
The main business objective is to identify high-performing and loss-making areas and provide an interactive dashboard that can support business decisions such as Q4 resource allocation.
The project combines Python, SQL, and Power BI to demonstrate an end-to-end data analysis workflow.
A retail business wants to understand:
- Which regions generate the most sales and profit?
- Which categories and sub-categories perform well?
- Which products and customers contribute the most profit?
- Which products or sub-categories are loss-making?
- How do sales and profit change over time?
- Are there areas where high sales do not translate into high profitability?
- Which areas should receive further attention when planning resources?
The project uses the Sample Superstore dataset.
- Rows: 9,994
- Columns: 21
- Data type: Retail sales transactions
- Order ID
- Order Date
- Ship Date
- Ship Mode
- Customer ID
- Customer Name
- Segment
- Country
- City
- State
- Region
- Product ID
- Category
- Sub-Category
- Product Name
- Sales
- Quantity
- Discount
- Profit
- Pandas
- Matplotlib
- Jupyter Notebook
Used for:
- Data loading
- Data cleaning
- Missing-value analysis
- Date feature engineering
- Exploratory Data Analysis (EDA)
- KPI calculation
- Regional analysis
- Category and sub-category analysis
- Discount analysis
- Customer analysis
- Product analysis
- Time-series analysis
- Root-cause investigation of loss-making areas
SQLite was used for querying the dataset.
SQL techniques used include:
- SELECT
- WHERE
- GROUP BY
- ORDER BY
- HAVING
- Aggregate functions
- CASE
- CTEs
- Window functions
- RANK()
- LAG()
- Date functions
- Customer analysis
- Product analysis
- Year-over-year analysis
Used to build an interactive 4-page dashboard containing:
- KPI cards
- Charts
- Slicers
- Top/Bottom-N analysis
- Profitability matrix
- Trend analysis
- Conditional formatting
Raw Dataset
↓
Data Loading & Cleaning
↓
Exploratory Data Analysis
↓
Python Analysis
↓
SQL Analysis
↓
Power BI Dashboard
↓
Business Insights
The Python analysis was performed using Pandas and Matplotlib.
- Dataset inspection
- Missing-value check
- Data type conversion
- Date feature engineering
- KPI calculation
- Regional performance analysis
- Category performance analysis
- Sub-category profitability analysis
- Discount analysis
- Time-based sales and profit analysis
- Segment analysis
- Region × Category analysis
- Product-level analysis
- Customer-level analysis
- Loss-making area investigation
Date-related features created during analysis included:
- Year
- Month
- Month Name
- Year-Month
The dataset was loaded into a SQLite database for structured querying.
Calculated:
- Total Sales
- Total Profit
- Total Quantity
Compared regions based on:
- Sales
- Profit
- Quantity
Analyzed:
- Sales
- Profit
- Profit Margin
- Loss-making sub-categories
Used SQL ranking to identify:
- Top products by profit
- Bottom products by profit
- Top 3 products within each category
- Loss-making products
Analyzed:
- Customer sales
- Customer profit
- Number of orders
- Customer profitability
- Loss-making customers
Used SQL date functions and window functions to analyze:
- Yearly sales
- Yearly profit
- Monthly sales
- Monthly profit
- Year-over-year sales growth
- Year-over-year profit growth
The Power BI report contains four pages.
Provides a high-level summary of business performance.
- Total Sales
- Total Profit
- Profit Margin
- Sales by Region
- Profit by Category
- Sales Trend Over Time
- Year slicer
- Region slicer
- Category slicer
Focuses on regional and category-level profitability.
- Sales by Region
- Profit by Region
- Regional & Category Performance Matrix
- Profit Margin by Category
- Profit by Sub-Category
- Region slicer
- Category slicer
Conditional formatting is used to make profitable and loss-making areas easier to identify.
Focuses on customer and product performance.
- Top 10 Customers by Sales
- Top 10 Customers by Profit
- Top 10 Products by Profit
- Bottom 10 Products by Profit
- Product Profitability Analysis Matrix
- Segment slicer
The profitability matrix provides:
- Sales
- Profit
- Profit Margin
at the category, sub-category, and product levels.
Analyzes how business performance changes over time.
- Sales Trend Over Time
- Profit Trend Over Time
- Monthly Sales Trend
- Monthly Profit Trend
- Profit Margin Trend
- Year slicer
The analysis identified several notable patterns.
- Phones generated the highest sales among the sub-categories.
- Copiers generated more total profit than Phones despite having lower sales.
- Tables generated high sales but had negative total profit.
- Bookcases and Supplies also recorded negative total profit.
- Paper, Labels, and Envelopes showed relatively high profit margins.
- Machines generated substantial sales but had a comparatively low profit margin.
The analysis demonstrates that high sales do not necessarily mean high profitability.
For example, some sub-categories generated substantial revenue while producing low or negative profit.
This makes profit margin and total profit important metrics alongside sales.
Discount and regional patterns were also examined for loss-making categories and products to identify areas requiring further analysis.
The project does not assume that discount or region is the cause of losses without supporting analysis.
Superstore Data Analysis/
│
├── README.md
│
├── data/
│ ├── Sample - Superstore.csv
│ ├── superstore.db
│ ├── customer_analysis.csv
│ ├── top_customers_sales.csv
│ ├── top_customers_profit.csv
│ └── segment_customer_analysis.csv
│
├── notebooks/
│ └── superstore_analysis.ipynb
│
├── sql/
│ └── superstore_analysis.sql
│
├── powerbi/
│ └── Superstore_Sales_Performance_Analysis.pbix
│
└── screenshots/
├── executive_overview.png
├── regional_category.png
├── customer_product.png
└── time_trends.png
The dashboard is designed to help stakeholders:
- Monitor overall sales and profitability
- Identify strong and weak regions
- Compare category performance
- Identify profitable and loss-making products
- Understand customer contribution
- Monitor sales and profit trends
- Investigate areas requiring further attention
- Support Q4 resource allocation discussions
Superstore Sales Performance Analysis
Analyzed 9,994 retail transactions using Python, SQL, and Power BI to evaluate regional, category, customer, product, and time-based sales and profitability patterns. Built a 4-page interactive Power BI dashboard with KPI cards, slicers, Top/Bottom-N analysis, profitability matrices, and trend analysis.
- Data Cleaning
- Exploratory Data Analysis
- Python
- Pandas
- Matplotlib
- SQL
- SQLite
- Aggregations
- GROUP BY
- HAVING
- CTEs
- Window Functions
- RANK()
- LAG()
- Date Analysis
- Customer Analysis
- Product Analysis
- Business Analysis
- Power BI
- Data Visualization
- Dashboard Development
- Business Insights
Bhumika Gadade
BTech – Electronics & Telecommunication Engineering
Interested in Data Analytics, AI, and Machine Learning.