Advertising and Temporal Influences on E-commerce Page Views: Evidence from Regression Models

Abstract

E-commerce growth relies heavily on data-driven insights for optimizing efficiency and profitability. This study utilized an e-commerce dataset to investigate key factors influencing e-commerce performance, particularly how to optimize operational strategies for profit maximization, employing three linear regression models to analyze the explanatory power of advertising expenditure and temporal factors on webpage views, with the first model assessing the impact of advertising spending independent of time effects, the second model incorporating time variables to observe changes in explanatory power, and the third model replacing the weekend variable with weekday to control for potential data volume bias, ultimately revealing that advertising spending accounted for only 27% of the variation in page views while temporal factors explained merely 1.5%, likely due to insufficient holiday data in the dataset, implying that other factors collectively contributed 71.5% of the explanatory power, yet the substantial role of advertising spending remained undeniable, warranting further in-depth research to fully address this question.