Machine Learning Analysis of Consumer Spending

Abstract

Focusing on the determination of key influential factors and predicting consumer buying patterns, the given study investigates the use of machine-learning methods to understand consumer spending behaviour.  We analyze a large data set that incorporates demographic, transactional, and economic variables with the help of various machine learning models, including the decision trees, random forests, support vector machines, and deep learning methodology.  The results indicate that a series of factors significantly influence consumer buying behaviors such as the level of income, preferences in product category and seasonal factors.  Random forest model performed better than the conventional methods of statistics and demonstrated maximum predicted accuracy among the models evaluated.  Also, feature importancy analysis revealed that prior purchases and consumer income were the most important determinants of spending behaviour.  The paper reads the way machine learning can successfully predict customer behaviour, providing informative data on targeted marketing strategies and personalised recommendations.  The findings mean that machine learning with consumer data analytics could enhance the quality of business decisions made in e-commerce and other sectors.