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Leveraging Machine learning and generative AI for content Engagement: An Exploration of drivers for the success of YouTube videos

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  • Mishra, Arindra Nath
  • Sengupta, Pooja
  • Biswas, Baidyanath
  • Kumar, Ajay
  • Coussement, Kristof

Abstract

Digital content creation has exploded in the last decade offering immense opportunities for brands and content creators. However, more research is needed on textual and aural content for determining video success using video analytics. Yet, data collection and analysis in this research context are labor-intensive. This study leveraged Generative AI (GenAI) models to automatically extract video transcripts and extract relevant metrics. We examined over 1055 YouTube videos released between 2021 and 2023 across three popular smartphones. We extracted semantic metrics from the transcript and comments to build models to explore the drivers of video success. We compared various GenAI-based measures and compared them to traditional methods. The results from this study confirm the superior performance of GPT4 over the benchmarks. The study’s theoretical contributions to the field of video-based content management and the managerial implications for practitioners in the field of video analytics are discussed.

Suggested Citation

  • Mishra, Arindra Nath & Sengupta, Pooja & Biswas, Baidyanath & Kumar, Ajay & Coussement, Kristof, 2025. "Leveraging Machine learning and generative AI for content Engagement: An Exploration of drivers for the success of YouTube videos," Journal of Business Research, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:jbrese:v:193:y:2025:i:c:s0148296325001535
    DOI: 10.1016/j.jbusres.2025.115330
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