Quantifying customer engagement : a biometric and visual analysis of responses to generated digital adverts

Abstract

Integrating artificial intelligence (AI) into marketing practices has catalysed a significant transformation in digital advertising, particularly through AI-generated content (AIGC). While this approach offers scalability and personalisation, questions remain regarding its effectiveness in eliciting genuine consumer engagement, especially trust, emotional resonance, and perceived authenticity. This study investigates the impact of AI-generated, human- designed (traditional), and hybrid advertisements on consumer engagement, employing a mixed-methods approach that integrates biometric and perceptual measures

Anchored in the Stimulus-Organism-Response (SOR) model (Huang & Rust, 2020), the research examines consumer reactions to static advertisements across three formats. Fifty-two participants, aged between 18 and 65, were exposed to themed advertisements in a controlled lab setting. Biometric data, including fixation duration, galvanic skin response (GSR), heart rate (HR), and blink rate, were recorded using Gazepoint GP3 eye-trackers and physiological sensors. Perceived authenticity (PA) was captured in real time using Self-Reporting Dials and supplemented with Likert-scale responses.