How Do Consumers Really Choose: Exposing Hidden Preferences with the Mixture of Experts Model
It addresses the need for better consumer choice modeling in marketing and management to improve personalization and decision-making, though it is incremental as it adapts an existing ML method to a specific domain.
This study tackled the problem of modeling complex consumer choice by introducing the Mixture of Experts (MoE) framework as a machine learning alternative to traditional models, demonstrating significantly enhanced predictive accuracy with large-scale retail data.
Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as multinomial logit (MNL) and mixed logit models, impose rigid parametric assumptions that limit their ability to capture the complexity of consumer decision-making. This study introduces the Mixture of Experts (MoE) framework as a machine learning-driven alternative that dynamically segments consumers based on latent behavioral patterns. By leveraging probabilistic gating functions and specialized expert networks, MoE provides a flexible, nonparametric approach to modeling heterogeneous preferences. Empirical validation using large-scale retail data demonstrates that MoE significantly enhances predictive accuracy over traditional econometric models, capturing nonlinear consumer responses to price variations, brand preferences, and product attributes. The findings underscore MoEs potential to improve demand forecasting, optimize targeted marketing strategies, and refine segmentation practices. By offering a more granular and adaptive framework, this study bridges the gap between data-driven machine learning approaches and marketing theory, advocating for the integration of AI techniques in managerial decision-making and strategic consumer insights.