AI-enabled consumer digital twins as a platform for research aimed at enhancing customer experience

Abstract

Consumer behavior is monitored and analyzed by innumerable obtrusive and non-obtrusive autonomous devices, technologies, surveys, models, and software. In recent years, with the advent of digitization, the research of consumer behavior has undergone a major transformation, yielding complex, extensive, and diverse consumer data. This data diversity is liable to hinder consumer research. Therefore, there is a clear need to integrate and synthesize the large-scale data centers conforming to predefined decision rules and research objectives. Against this backdrop, this contribution proposes to the marketing and consumer research community a new platform for data fusion and consumer modeling—consumer digital twins (CDTs). Although numerous research studies have been published on human digital twins (HDTs), none have been conducted in the management and consumer domains. The study aims to bridge two perspectives: on the one hand, the authors acknowledge the value of CDT as a consumer data fusion platform, while on the other hand, they build on previous scholarship to propose a conceptual framework for implementing the platform in future research, using as an example a CDT designed for customer journey optimization.