Exploring Machine Learning's Potential in Predicting Consumer Behavior for Captivating Marketing

Abstract

This research study investigates the use of machine learning algorithms to anticipate the behavior of online shoppers in e-commerce. The research uses a dataset containing various consumer factors, such as demographic information, previous purchases, and internet behavior. The subject of this research is the effectiveness of machine learning techniques such as random forests, support vector machines, and neural networks in predicting consumer behavior, such as purchase choices and product preferences. This is accomplished by the pre-processing of data, the selection of features, and the training of models. The findings indicate that the machine learning models accurately predicted consumer behavior with high accuracy and recall rates, with Random Forest beating the other investigated algorithms. The research shows several vital aspects, such as customer demographics, purchase history, and interaction with websites, significantly contributing to accurately forecasting consumer behavior. In conclusion, the research provides empirical evidence that machine learning algorithms can effectively predict customer behavior and improve personalized marketing methods in the e-commerce industry. The research results significantly affect businesses that want to employ data-driven insights to better consumer targeting and marketing activities.

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