IDENTIFICATION OF FACTORS INFLUENCING RETAIL SALES

Abstract

The retail sector is an integral part of the U.S. economy that continues to grow. It was reported that sales revenue
achieved $7.2 trillion in 2024. This study will use a two-year retail sales dataset including 200,000 transactions across different regions
of the United States in order to analyze sales performance and identify the key factors influencing retail revenue. Identification of factors
influencing revenue in retail is important. Because it helps to make proper decision-making. To satisfy research objectives, exploratory
data analysis (EDA) was applied to understand the structure of data, define relationship patterns, and do data preprocessing to work with
cleaned data. Also, hypothesis testing was made to analyze important variations and relationships among selected variables. Moreover,
simple and multiple regressions were applied alongside ordinary least squares (OLS) to see the impact results of some variables on
the revenue. The regression results show that Unit Price, Quantity and category of products have an impact on the retail revenue. The
results also show that usage of multiple predictors suggests greater explanatory power and increased accuracy of prediction compared
to models that rely on single predictors. The multiple regression model was chosen as the main approach for predictive analysis and
revenue forecasting.