Superstore Sales EDA – Business Performance Analysis

• Business Problem With growing demand and cut-throat competition in the retail market, a Superstore Giant aims to understand what works best for their business. The objective is to identify which products, regions, categories, and customer segments should be targeted or avoided to maximize profitability and ensure sustainable growth.

• Project Objectives Identify high-performing and loss-making products Analyze regional and state-level profitability Evaluate category and sub-category performance Understand customer segment contribution Assess the impact of discounts on profitability Provide actionable business recommendations

• Dataset Description The dataset contains retail transaction data with the following key attributes: Order Details: Order ID, Order Date, Ship Date, Ship Mode Customer Details: Customer ID, Customer Name, Segment Geography: Country, Region, State, City Product Details: Category, Sub-Category, Product Name Financial Metrics: Sales, Quantity, Discount, Profit

• Tools & Technologies Python Pandas & NumPy Matplotlib & Seaborn Jupyter Notebook

• Analysis Workflow Business Understanding Data Loading & Inspection Data Cleaning & Preparation Feature Engineering Univariate Analysis Category & Sub-Category Analysis Regional & State-wise Performance Customer Segment Analysis Discount vs Profit Analysis Product-Level Profitability Multivariate Correlation Analysis Business Insights & Recommendations

• Key Insights

High sales do not always translate into high profits Certain sub-categories contribute disproportionately to losses Some regions and states consistently underperform Excessive discounts significantly reduce profitability Consumer and Corporate segments drive the majority of revenue A small set of products generates most of the profit

• Business Recommendations

Target: High-margin products, profitable regions, and strong customer segments Optimize: Discount strategies to prevent margin erosion Avoid or Re-evaluate: Loss-making products, sub-categories, and regions Focus on profit-driven growth instead of revenue-only expansion

• Conclusion

This exploratory data analysis provides data-driven insights to help the Superstore optimize pricing, product strategy, regional focus, and customer targeting. Implementing these recommendations can significantly improve overall profitability and operational efficiency.

• Dataset Source

The dataset used in this project is publicly available on Kaggle: Superstore Sales Dataset https://www.kaggle.com/datasets/vivek468/superstore-dataset-final