Predicting E-commerce Revenue using Machine Learning and User Behavior Analysis

Abstract

The digital economy, and in particular, the e-commerce sector, has experienced significant growth in the last few decades [1]. The ability to accurately predict revenue in this burgeoning sphere is crucial for businesses to maximize profitability and to maintain a competitive advantage [2]. However, traditional forecasting methods, primarily based on historical data and statistical techniques, often fail to accurately anticipate the dynamic and nonlinear nature of e-commerce revenue. These techniques, such as time-series analysis, linear regression, and moving average models, often rely on the assumption of linearity and stationarity, which may not hold true in the unpredictable landscape of e-commerce, characterized by rapidly changing consumer behavior, market trends, and competitive dynamics [3].