This project analyzes sales, profitability, and customer behavior for Global Superstore, a leading international retailer operating in 147 countries. Using Tableau, Python, and Excel, I explored sales trends, regional performance, and product profitability to provide data-driven recommendations.
- Source: Global Superstore Dataset
- Size: 51,290 records
- Fields: Order Date, Country, Sales, Profit, Shipping Cost, Product Category, Customer Segment, etc.
- Python (Pandas) – Data cleaning
- Tableau – Data visualization and dashboard creation
- Excel – Data validation and quick exploration
- Which countries generated the most profit?
- Which subcategories have the highest shipping costs?
- What is Nigeria's profitability compared to other African countries?
- Which customer segments show the most returns?
- Who are the most valuable customers?
This Tableau dashboard provides a high-level summary of Global Superstore’s performance across regions, with a focus on profitability, shipping cost, and customer returns.
- Revenue Optimization: Identified high-profit segments, products, and customer groups for targeted marketing.
- Cost Reduction: Uncovered subcategories and regions with high shipping costs and return rates, guiding cost-control efforts.
- Growth Strategy: Used regional performance data to pinpoint high-potential markets such as the U.S., India, and China, and to identify strong product categories for future investment.
- Executive Reporting: Delivered clear, data-driven recommendations to guide strategic decisions.
- Invest in top-performing regions (U.S., India, China) by offering faster shipping or targeted promotions.
- Investigate shipping inefficiencies in low-margin countries and explore local warehousing options.
- Enhance customer retention strategies for high-value segments (e.g., loyalty programs for corporate buyers).
Open for educational and portfolio purposes.