Purchase prediction based on emotional states and contextual factors in retail physical using Machine Learning
DOI:
https://doi.org/10.51247/st.v8iS2.647Keywords:
Machine learning, physical retail, emotional states, contextual factors, purchase predictionAbstract
This study examines the potential of machine learning to predict shopping behavior in physical retail environments, integrating emotional variables and contextual factors. Through a literature review of recent academic literature, predictive models that transcend traditional demographic variables and incorporate affective dimensions such as emotional states, and contextual dimensions such as time of day or store location, are analyzed. It is recognized that while e-commerce has advanced in the use of predictive technologies, face-to-face retailing still faces limitations in collecting meaningful data. The analysis reveals both the strategic value of these systems in optimizing the consumer experience and the emerging ethical dilemmas related to emotional manipulation and intensive use of data. The findings contribute to the understanding of the consumer as a complex subject and provide a conceptual foundation for future research and practice in sensory and predictive marketing applied to physical spaces.
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Copyright (c) 2025 Kleber Francisco Franco-Santacruz, Bill Jonathan Serrano-Orellana
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