广告
声望
水准点(测量)
多样性(政治)
人工智能
计算机科学
营销
心理学
业务
社会学
语言学
哲学
大地测量学
人类学
地理
作者
Yusuf Oc,Kirk Plangger,Sean Sands,Colin Campbell,Leyland Pitt
摘要
Abstract Many luxury brands are investing heavily in creating dynamic video content to actively engage consumers. While it is straightforward to calculate the views or “likes” from a particular campaign to benchmark performance, analyzing consumers' comments on luxury brands' dynamic video content presents a challenge due to the unstructured nature of natural language and large comment volumes. Previous studies utilizing machine learning and artificial intelligence (AI) have not adequately examined the impact of brand types, brand luxuriousness, and consumer diversity. To address this research gap, this article tests a conceptual framework with over 29,000 comments from 88 YouTube campaigns for nine luxury brands using a combination of automatic text and image analyses. The results indicate significant differences in comments' psycholinguistic nature depending on the brand's luxuriousness (premium, prestige, and exquisite) and Copelandian classification (convenience, shopping, and specialty), as well as consumers' demographic characteristics (age, gender, and ethnicity). These findings suggest that brand managers can use machine learning and AI methods to better tailor dynamic content creation to further engage diverse target segments by refining the campaign message to encourage additional engagement.
科研通智能强力驱动
Strongly Powered by AbleSci AI