Analysis of Sales, Profitability, and Customer Behavior in the Superstore Dataset

Abstract

Introduction: The analysis of sales, profitability, and customer behavior has become essential for improving decision-making in the retail sector, especially through the use of business intelligence tools that allow data to be explored visually and analytically.
Objective: To analyze the factors that influence business performance in the retail sector using the Sample Superstore dataset, with emphasis on the effect of discounts on profitability, customer segment behavior, and geographic differences in sales and profit margins.
Method: A descriptive and analytical approach was applied using Microsoft Power BI as the main tool for data visualization and exploration. The analysis was structured around four research questions related to product category performance, discount impact, customer segment profitability, and regional opportunities for improvement.
Results: The results show that the Technology category generates the highest sales and profit levels, while some subcategories, such as Tables, present significant losses despite having considerable sales volume. A negative relationship was identified between discounts and profitability, especially when discounts exceed moderate levels. The Consumer segment generates the highest sales volume, whereas the Corporate segment shows more stable and profitable behavior. In addition, states such as Texas and Illinois were identified as areas with high sales volume but low profit margins.
Conclusions: The findings suggest the need to optimize pricing strategies, control discount policies, strengthen high-value customer segments, and improve regional commercial management in order to increase profitability and support more effective decision-making in the retail sector.