Multivariate Modeling for Accurate Prediction of Consumer Preferences in Global Retail Markets

Abstract

Global retail faces persistent challenges in predicting consumer preferences amid fragmented markets and diverse data sources. This study examines how multivariate consumer modeling—integrating behavioral data, preference patterns, and cross-market attributes—enhances prediction accuracy across advanced and emerging economies, moderated by market context complexity. Analysis utilizes firm-level secondary data from the Retail Consumer Preference Multivariate Dataset, covering 43 large retailers from a global population of 1,360 firms. Prediction accuracy is modeled multidimensionally, with behavioral data integration, preference pattern analysis, cross-market attribute mapping as predictors, and market complexity as moderator. Integrated behavioral data boosts forecast precision and decision reliability; preference analysis improves segmentation clarity and demand alignment; attribute mapping enhances cross-regional robustness. Market context complexity weakens these effects unless modeling exhibits high coherence and depth. The RetailPreference Multivariate Model demonstrates prediction accuracy emerges from coordinated analytical architecture rather than isolated techniques, explaining divergent outcomes from similar analytics investments. Findings advance consumer analytics theory and guide global retail strategy.