Analyzing Consumer Decision-Making in Digital Environments Using Random Forest Algorithm and Statistical Methods

Abstract

In an era characterized by the rapid digital transformation of the marketplace, understanding consumer behavior is essential for effective decision-making and the development of marketing strategies. This study investigates the impact of demographic attributes such as age, income, education, and lifestyle preferences, alongside social media engagement, on the consumer decision-making process in the Al-Qassim region of Saudi Arabia. A survey was distributed, gathering responses from 684 participants. The study specifically tests the hypotheses that demographic factors significantly influence each stage of the decision-making journey: problem recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behavior, with social media engagement acting as a mediating factor in these stages. By utilizing management information systems to analyze this comprehensive dataset, a Random Forest Classifier was employed, achieving an overall accuracy of 88% and revealing significant correlations between demographic characteristics and consumer behavior. The model demonstrated particularly strong performance in the Evaluation of Alternatives stage, with a precision of 0.90 and a recall of 0.95. Additionally, the findings underscore the critical role of social media engagement in enhancing consumer awareness and influencing purchasing decisions. This study provides actionable insights for marketers in the Al-Qassim region, equipping them with the necessary tools to optimize their strategies in the rapidly evolving digital landscape, ultimately improving consumer satisfaction and fostering long-term loyalty.