City Research Online

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.

Publication Type:	Article
Additional Information:	This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2023 The Authors. Psychology & Marketing published by Wiley Periodicals LLC.
Publisher Keywords:	automatic text and image analysis, brand luxuriousness, luxury marketing communications, machine learning and artificial intelligence (AI), nature of electronic word‐of‐mouth (eWOM), social media video advertising, YouTube
Subjects:	H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HM Sociology Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Bayes Business School > Management
SWORD Depositor:	Symplectic Administrator

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