Judul: Comparing Visual Attention, AI-Reface, and Human Endorsers in Video Advertising: Heatmaps and Tagging Analysis
Abstrak: Understanding the differential impact of human and AI-Reface endorsers in digital advertising is crucial for optimizing marketing strategies in an increasingly digital landscape. With the proliferation of AI technologies, advertisers are keen to explore how AI-generated faces compare to human endorsers in terms of capturing attention, enhancing brand recall, and influencing purchasing decisions. This research, focusing on Shopee's video advertisements featuring both human and AI-Reface endorsers, delves into consumer engagement patterns and the effectiveness of these endorsers. By employing advanced techniques like eye tracking, visual tagging, and post-exposure surveys, the study provides comprehensive insights into how different demographic groups respond to these endorsements, ultimately guiding more effective advertising strategies. The study employed a robust methodology, leveraging eye tracking, visual tagging, and post-exposure surveys to gather quantitative and qualitative data on viewer engagement and perception. Mixed respondent groups, segmented by gender, were exposed to Shopee video advertisements featuring either a human endorser (Melody Nurramdhani Laksani) or an AI-Reface endorser (Prilly Latuconsina). Eye tracking technology measured where and how long viewers focused on specific elements within the ads, while visual tagging allowed respondents to actively indicate which elements captured their attention. Post-exposure surveys provided deeper insights into viewers' perceptions of brand elements, endorsers, and promotional content, as well as their likelihood to purchase the advertised products. Data was processed and analyzed to identify patterns and differences in engagement and recall between the human and AI-Reface endorser videos across different respondent groups. The analysis revealed nuanced differences in how viewers engaged with the brand, endorser, and promotional elements in the videos. Eye tracking and visual tagging data showed that both endorsers effectively captured viewer attention, with slight variations in engagement levels. Human endorsers garnered slightly higher attention in specific scenes, but AI-Reface endorsers were also highly effective, indicating their potential as viable alternatives. Promotional elements consistently attracted the highest viewer engagement, underscoring their importance in digital advertising. Brand elements, while crucial for recall, received less immediate attention, suggesting a need for more integrated branding strategies. Post-exposure surveys highlighted that both human and AI-Reface endorsers positively influenced brand perception and recall. However, the human endorser slightly edged out the AI-Reface endorser in terms of influencing purchase decisions, possibly due to the inherent trust and relatability associated with human faces. These findings suggest that while AI-Reface endorsers are almost as effective as human endorsers, the latter's slight advantage in driving purchase intent could be leveraged more strategically. The results also indicated that the visibility and prominence of brand elements could be enhanced to improve overall ad effectiveness. To maximize the effectiveness of digital advertisements, a balanced approach that integrates the strengths of both human and AI-Reface endorsers is recommended. Advertisers should continue utilizing human endorsers for their relatability and trust-building attributes, particularly in campaigns aimed at directly influencing purchase decisions. However, AI-Reface endorsers can be strategically employed in scenarios where scalability and cost-efficiency are paramount, without significantly compromising engagement and brand perception. Promotional content should remain a focal point in ad design, given its high engagement levels. Enhancing the visibility and prominence of brand elements, possibly by
Keyword: Intelligence, Brand, Endorser, Neuromarketing, Promotion, AI-Reface, Artificial