Data-driven message optimization in dynamic sports media: an artificial intelligence approach to predict consumer response

Artificial intelligence (AI) and big data have the potential to promote advancement across various industries. Sport management and marketing have also significantly transformed due to rapid technological advances such as those in AI and big data analytics. Especially sports companies, however, are still underutilizing the potential of AI. At the same time, considering the existing sport marketing research, the effectiveness and optimization of dynamic marketing stimuli in dynamic sport media settings remains unclear. This study aims to assess the differences between two AI models' predictive capabilities with and without access to consumers' biometric data when forecasting the influence of game features on consumers' responses. Academic theoretical models indicate that individual biometric features have a considerable influence on consumers' responses; nevertheless, it remains impractical for companies to access these data concerning message effectiveness and ROI evaluation. Therefore, the study attempts to enhance the feasibility of message optimization for companies by trialing a real-time prediction derived from game features alone, exemplifying how much predictive capability is lost by non-available consumer data. Two supervised machine learning models (one initial, primarily theoretical based model; one adapted model due to available data) were trained to reanalyze large-scale eye tracking and game-related data, resulting in high predictive accuracy and appropriate applicability of the models. Both models were able to predict consumers' responses with over 90% accuracy (initial model: 96%; adapted model: 94%). This study exemplifies AI usage in sport marketing and management, enabling companies to implement more effective marketing messages and strategies for their sponsorship based on real-time evaluation.