Profiling the AI speaker user: Machine learning insights into consumer adoption patterns.

ABSTRACT: The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do so, our analysis employs decision trees, random forests, support vector machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). The final XGboost model, which performed the best among the other machine learning models, specifically forecasts individuals aged 45-50 and 60-65, who are active on social networking platforms and have a preference for varied progra

SUBMITTER: Choi Y 

PROVIDER: S-EPMC11654937 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature