Consumer behavioural analytics in metaverses using machine and deep learning: a literature review and research agenda

Pedro Mota Veiga, Sara Santos, Julien Diogo, João J. Ferreira and Cristina Fernandes

Chapter 8 in Handbook of Technological Forecasting and Roadmapping, 2026, pp 175-206 from Edward Elgar Publishing

Abstract: In recent years, marketers have begun exploring the new opportunities for generating customer profiles provided by emerging machine learning technologies capable of identifying the content of images and videos. Machine learning can help business decision-makers define target markets and equips marketing managers with a combination of strategies to induce customers to make purchases, and particularly helping marketing managers understand the behaviours and journeys of their consumers. Despite the great relevance of this field of study, the literature remains fragmented and appears to lack any literature review on machine and deep learning in the context of consumer behaviours in virtual environments. Therefore, our objective here is to fill this gap by identifying, exploring and systematising the main themes, contributing to deepening the literature through identifying priority areas and setting out a roadmap for applying machine and deep learning to behavioural contexts. Following the application of bibliometric methods and content analysis, we found four approaches to studying machine learning and deep learning in consumer behaviour contexts in virtual environments: i) immersive and virtual shopping experiences, ii) data analysis and behavioural prediction; iii) technology and innovation in customer service, and iv) strategic customer management.

Keywords: Augmented Reality; Consumer Behavioural Analytics; Data Analytics; Deep Learning; Machine Learning; Metaverse; Virtual Reality (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035330843
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