live dashboard link - https://public.tableau.com/app/profile/mayank.choudhary5404/viz/sale_17774612070440/Dashboard1?publish=yes
This project analyzes the Global Superstore dataset to understand sales performance, profitability, and the impact of discounting strategies across different regions and product categories.
The project follows an end-to-end data analytics pipeline including data cleaning (Python), querying (SQL), analysis, and visualization using Tableau.
Retail Analytics / E-Commerce
Retail businesses often struggle to maintain profitability while offering discounts and managing diverse product categories across multiple regions.
This project aims to identify:
- Factors affecting profit
- Impact of discounts
- Regional and category-wise performance
How do discounts, product categories, and regional performance impact overall profitability?
| Attribute | Details |
|---|---|
| Source | Kaggle |
| Dataset | Global Superstore Dataset |
| Link | https://www.kaggle.com/datasets/apoorvaappz/global-super-store-dataset |
| Format | CSV |
| Size | 50,000+ rows |
- Sales
- Profit
- Discount
- Category
- Sub-Category
- Country
- Region
- Order Date
File: superstore_cleaning.ipynb
Steps performed:
- Removed duplicate records
- Handled missing values
- Converted date columns
- Standardized column names
- Created derived metrics
File: superstore_queries.sql
- Used SQL queries for data extraction and aggregation
- Performed analysis on sales and profit trends
File: yearly_trend.png
Shows increasing sales trend over time.
File: category_sales_profit.png
Highlights sales vs profit across categories.
File: discount_vs_profit.png
Shows negative relationship between discount and profit.
File: top_countries.png
Shows top-performing countries by sales.
-
KPI Cards:
- Total Sales: $12.64M
- Total Profit: $1.47M
- Total Orders: 25,035
- Profit Margin: 11.61%
-
Sales Trend (Monthly)
-
Category-wise Sales & Profit
-
Discount vs Profit Analysis
-
Sales by Country Map
- Year
- Category
Dashboard Screenshot: (Add your main dashboard image here)
- High discounts significantly reduce profit
- Some categories generate high sales but low profit
- Sales are concentrated in a few countries
- Profit margins vary across regions
- Sales growth does not match profit growth
- Certain products lead to consistent losses
- Consumer segment drives most revenue
- Discount-heavy strategies reduce efficiency
| Insight | Recommendation | Impact |
|---|---|---|
| High discounts | Optimize discount strategy | Increase profit |
| Low-profit categories | Improve pricing | Better margins |
| Regional imbalance | Focus on strong markets | Revenue growth |
| Loss-making products | Remove/improve | Reduce losses |
Applying these recommendations can improve profitability by 10–20% through better pricing strategies and reduced losses.
- Dataset is historical
- No real-time data
- Limited customer behavior data
- Add machine learning for forecasting
- Real-time dashboards
- Advanced customer segmentation
- Ipshita Patel
- Mayank Choudhary
- Kumar Manak
- Krish Mukesh Jain
- Rohit Dahiya
Superstore_data/
│
├── data/
│ ├── cleaned_superstore.csv
│ └── Global_Superstore.csv
│
├── superstore_cleaning.ipynb
├── superstore_queries.sql
├── superstore.db
│
├── category_sales_profit.png
├── discount_vs_profit.png
├── top_countries.png
├── yearly_trend.png
│
└── README.md
- Python (Pandas, NumPy)
- SQL
- Tableau
- GitHub
This project demonstrates how raw data can be transformed into meaningful business insights using data analytics and visualization tools. The findings help improve decision-making in pricing, product strategy, and regional performance.
| Member | ETL | EDA | Dashboard | Report | Insights |
|---|---|---|---|---|---|
| Ipshita | ✔ | ✔ | ✔ | ✔ | |
| Mayank | ✔ | ||||
| Kumar | ✔ | ||||
| Krish | ✔ | ||||
| Rohit | ✔ | ✔ |
This project is an original work completed by the team members listed above.