Transforming consumers’ IoT data into behavioral insights with AI-enabled Consumer Digital Twins (CDT) for marketing analytics

In the contemporary era of evolving and diverse human consumption behaviors and advanced data collection technologies, it is imperative to research consumer sentiments and behaviors using non-intrusive data-generating technologies for analysis, the design of relevant algorithms, and strategy formulation to meet individualized needs and support precision marketing. The Internet of Behavior (IoB), a successor to the IoT, and the newly introduced Internet of Consumer Behavior (IoCB) represent a growing interdisciplinary field at the intersection of technology, data analytics, and human psychology. It focuses on gathering and analyzing vast amounts of data from smart devices to identify and understand behavioral patterns. However, the heterogeneity and complexity of such data pose significant challenges to effective research and analytics. To address these challenges, this paper introduces Consumer Digital Twins (CDTs) as an advanced platform for data integration, augmentation, interoperability, and modeling. It presents a conceptual model based on the IoB and the Human Digital Twin (HDT), encapsulated within the CDT/IoCB framework. This model provides insights and guidance for marketing analytics that aim to deliver personalized offerings, outlining key theoretical, ethical and practical implications, limitations, and future research directions for advancing research.