Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper

An excessive amount of data is generated daily. A consumer’s journey has become extremely complicated due to the number electronic platforms, devices, information provided, and providers. The need for artificial intelligence (AI) models that combine marketing computer science methods imperative classify users’ needs. This work bridges gap between by introducing current trends AI on data. It examines consumers’ behaviour using a decision-making model, which analyses choices helps decision-makers understand their potential clients’ model able predict consumer both in digital physical shopping environments. combines decision trees (DTs) genetic algorithms (GAs) through one wrapping technique, known as GA wrapper method. Consumer from surveys are collected categorised based research objectives. was found perform exceptionally well, reaching classification accuracies above 90%. With regard Gender, Household Size, Monthly Income classes, it manages indicate best subsets specific genes affect making. These classes were be associated with set variables, providing clear roadmap decision-making.