A RESEARCH REGARDING DESIGNING IMPORTANT CROSS THE IMPACT IN PURCHASE INCIDENCE: AN ANALYSIS OF ARTIFICIAL NEURAL NETWORK METHODS AND MULTIVARIATE PROBIT DESIGNING

Abstract

The goal of this research is to provide light on customer habits across similar product types by simulating massive cross-effects in purchase occurrence. Complex cross-category interactions are tested to see how effectively various modeling tools, such as Multivariate Probit (MVP) and Artificial Neural Networks (ANNs), capture them. In contrast to ANNs' renowned flexibility in handling big datasets and nonlinear interactions, MVP models provide a transparent statistical foundation based on economic theory. Research compares the precision of these methodologies' predictions, the lucidity of their presentations, and their capacity to grasp the subtleties of consumer behavior across different product categories. Even while ANNs do very well in terms of prediction accuracy, the results show that MVP models provide much better interpretability and theoretical consistency. The results provide useful information for marketing and decision-making while also contributing to the expansion of consumer choice modeling approaches. This research analyzes and contrasts the efficacy of ANN techniques with MVP models to evaluate large-scale cross effects on sales incidence. By comparing and analyzing two state-of-the-art approaches, this study aims to provide insight on their respective capabilities in identifying complex linkages and predicting consumer behavior in big datasets. The purpose of this research is to evaluate the efficacy of applying ANN and MVP algorithms to purchase incidence data in order to get more relevant and useful insights about consumer behavior. Focusing on model correctness, computational efficiency, and interpretability, this comparison study seeks to aid researchers doing marketing or consumer research in choosing the most suitable modeling methodologies.