Skip to content

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.

Notifications You must be signed in to change notification settings

hutchay/global-superstore-capstone

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

22 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Global Superstore Sales and Profitability Analysis

Project Overview

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.

Dataset

  • Source: Global Superstore Dataset
  • Size: 51,290 records
  • Fields: Order Date, Country, Sales, Profit, Shipping Cost, Product Category, Customer Segment, etc.

Tools Used

  • Python (Pandas) – Data cleaning
  • Tableau – Data visualization and dashboard creation
  • Excel – Data validation and quick exploration

Key Questions Answered

  • 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?

📊 Dashboard Overview

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.

image

Strategic Business Outcomes

  • 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.

Recommendations

  • 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).

Deliverables

License

Open for educational and portfolio purposes.

About

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.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published