Predicting Impulse Buying Behavior through AI-Enhanced Digital Marketing Analytics

Abstract

One of the main factors that have disrupted the business world is digital marketing, which has significantly changed the way companies operate and has been the center of the new business world. Digital marketing enables companies to access and utilize different tools and methods that facilitate them in targeting customer groups and influencing their decisions. In the top of everything, the use of Artificial Intelligence (AI) not only creates convenience for marketers in attracting consumers but also provides them with advanced tools that facilitate the process of predictive analytics, machine learning, and AI-based recommendation systems; thus companies are not only capable of knowing but also forecasting their consumers’ buying habits. Basically, the combination of AI as the very first step of the experience to get familiar with human emotions and to provide the most exact measurement of impulse purchasing factors like trust, attitude, hedonic motivation, and fear of missing out (FOMO) is a remarkable marketing research revolution. The major characteristic of an AI-driven model for the upcoming research which practically represents and theoretically maps the connection between digital marketing strategies and impulsive buying behavior is the foremost feature. After the comparative study, the research results point out that personalized recommendations and real-time interactive features have the most substantial links to impulse buying, whereas the hybrid recommendation models provide the closest predictions for all the evaluation metrics. The paper deals with the delivery of the research results on the effectiveness of different digital marketing strategies, the targeting of impulsive consumer behavior, and the reliance of AI in facilitating the predictive accuracy and creating actionable insights