跳到主要內容

簡易檢索 / 詳目顯示

研究生: 魏守芸
Wei, Shou-Yun
論文名稱: 消費電子品牌的YouTube熱門影片行銷策略
YouTube Trending Video Marketing Strategy for Consumer Electronics Brands
指導教授: 朴星俊
Park, Sung-Jun
口試委員: 陳文鑫
Chen, Wen-Shin
蔡葵希
Cook, Christine Linda
學位類別: 碩士
Master
系所名稱: 商學院 - 國際經營管理英語碩士學位學程(IMBA)
International MBA Program College of Commerce(IMBA)
論文出版年: 2025
畢業學年度: 113
語文別: 中文
論文頁數: 54
中文關鍵詞: Youtube 發燒影片品牌消費電子數位行銷
外文關鍵詞: Youtube trending video, Viewer engagement
相關次數: 點閱:271下載:5
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 隨著全球活躍用戶超過 24.9 億,YouTube 已成為影音行銷的重要平台。消費者越來越依賴 YouTube 影片來影響購買決策,特別是在消費性電子產品領域。因此,了解如何優化影片在 YouTube 發燒影片排行榜(Trending List)上的表現,對該產業的品牌行銷人員而言至關重要。本研究旨在驗證 YouTube 的演算法優先順序,並透過分析影響科技類影片在熱門趨勢榜上維持時間的因素,提供具體的行銷建議。研究聚焦於三個地區——南韓、英國和美國,分析了 2022 年 6 月至 2024 年 1 月期間,共 1,232 部發燒影片的資料,探討觀眾參與度指標、內容類型及地區差異對影片持續在發燒排行榜上存在時長的影響。

    研究結果首先顯示,按讚數增長是影響熱門趨勢維持時間的最關鍵因素,而觀看數增長的影響相對較小。此外,品牌製作內容(Brand-Generated Content,BGC)通常能獲得較高的觀看次數,而使用者生成內容(User-Generated Content,UGC)則在按讚數和留言數方面表現更為突出。

    地區差異也相當明顯——南韓的影片在熱門趨勢榜上的維持時間幾乎是美國和英國等西方市場的兩倍。這些發現能幫助消費性電子品牌根據不同地區的受眾特性,優化其 YouTube 行銷策略。


    With over 2.49 billion active users worldwide, YouTube has become a critical platform for video marketing. Consumers are increasingly relying on YouTube videos to influence their purchasing decisions, particularly in the consumer electronics sector. Understanding how to optimize video performance on YouTube's Trending list has become essential for brand marketers in this industry. This study aims to validate YouTube's algorithmic priorities and provide actionable marketing insights by analyzing the factors that influence the duration of technology-related videos on the Trending list. Focusing on three regions—South Korea, the United Kingdom, and the United States—the research examines a dataset of 1,232 trending videos from June 2022 to January 2024. It explores the impact of viewer engagement metrics, content type, and regional differences on trending duration.

    The findings first reveal that the growth in likes count is the most significant factor affecting trending duration, while the growth in view count has little impact. Additionally, BrandGenerated Content (BGC) typically achieves higher view counts, whereas User-Generated Content (UGC) outperforms in likes and comments.

    Regional differences are prominent, with South Korean videos trending for nearly twice as long as those in Western markets, the US and UK. These insights can help consumer electronics brands refine their YouTube marketing strategies by tailoring their approach to specific regional audiences.

    TABLE OF CONTENTS

    1. Introduction 1
    1.1 Research Motivation 1
    1.2 Research Objectives 2
    1.3 Research Questions 3

    2. Literature Review 7
    2.1 YouTube as a Marketing Platform 7
    2.2 YouTube Trending List 7
    2.3 Viewer Engagement and Video Performance 8
    2.4 User-Generated Content vs. Brand-Generated Content 9
    2.5 Cross-Country Analysis of YouTube Trending Videos 9

    3. Research Methodology 10
    3.1 Research Design 10
    3.2 Data Collection 11
    3.3 Data Preprocessing 17
    3.4 Data Analysis 19
    3.5 Variables and Measurement 20
    3.6 Data Analysis Techniques 23

    4. Results & Discussion 24
    4.1 Overview of Collected Data 24
    4.2 Analysis of Viewer Engagement and Trending Days 25
    4.3 Analysis of BGC & UGC 28
    4.4 Cross-Country Analysis of Trending Video Performance 30

    5. Conclusion 39
    5.1 Summary 39
    5.2 Theoretical Implications 40
    5.3 Managerial Implications 42
    5.4 Limitations & Recommendations for Future Research 44

    References 47
    Appendices 50

    Google. (n.d.). Trending on YouTube. Youtube Help https://support.google.com/youtube/answer/7239739?hl=en

    The YouTube Team. (n.d.). (2021, November 10). An update to dislikes on YouTube. Youtube Official Blog https://blog.youtube/news-and-events/update-to-youtube/

    Nitya, H., Prathima, T., & Sugamya, K. (2024). Decoding YouTube’s Trends: Unveiling Viral Content Secrets. 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0 (pp. 1–13). IEEE. https://doi.org/10.1109/OTCON60325.2024.10687676

    Lu, J. (2023). Trending videos on YouTube. 2nd International Conference on Management and Education, Humanities and Social Sciences (MEHSS 2022), 7. 84–91. https://doi.org/10.54097/ehss.v7i.4016

    Pugalendhi, R., & Nazar, N. (2024). Decoding YouTube's trending videos: Factors, implications, and insights. International Conference on Global Synergy Summit - Bridging the Disciplines in Management, Research, Engineering, Education, and Humanities. 233–234. https://www.researchgate.net/publication/378691027_DECODING_YOUTUBE'S_TRENDING_VIDEOS_FACTORS_IMPLICATIONS_AND_INSIGHTS

    HOSSAIN, M. I., SABBIR, M. M., & KIM, H. J. (2023). Unveiling the Effect of TechTubers’ Unboxing Videos on Consumer Buying Behavior. Journal of Economics, Marketing and Management, 11(4), 41–52.
    https://doi.org/10.20482/JEMM.2023.11.4.41

    Zhou, Y., Ahmad, Z., Alsuhabi, H., Yusuf, M., Alkhairy, I., & Sharawy, A. M. (2021). Impact of YouTube Advertising on Sales with Regression Analysis and Statistical Modeling: Usefulness of Online Media in Business. Computational intelligence and neuroscience, 2021(1), 1–10. https://doi.org/10.1155/2021/9863155

    Gupta, H., & Singh, S. (2017). Social Media in Contemporary Marketing: YouTube Advertising for the Guerrillas. Media Watch, 8(3), 413–422.
    https://doi.org/10.15655/mw_2017_v8i3_49145

    Diwanji, V.S., & Lee, J. (2022). Comparing the Effects of User Generated Video Reviews and Brand Generated Advertisements on Consumer Decisions on YouTube. Journal of Applied Marketing Theory, 9(1), 48–75.
    https://doi.org/10.20429/jamt.2022.090105

    Hussain, M. N., Bandeli, K. K., Tokdemir, S., Al-Khateeb, S., & Agarwal, N. (2018). Understanding digital ethnography: Socio-computational analysis of trending YouTube videos. SOTICS 2018 : The Eighth International Conference on Social Media Technologies, Communication, and Informatics, 21–26. https://personales.upv.es/thinkmind/dl/conferences/sotics/sotics_2018/sotics_2018_1_30_60011.pdf

    Manikandan, P., Manimuthu, A., Sharmila Rajam, J., & Sathya Narayana Sharma, K. (2022). Prediction of YouTube View Count using Supervised and Ensemble Machine Learning Techniques. 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 1038–1042). IEEE.
    https://doi.org/10.1109/ICACRS55517.2022.10029277

    Huang, S., & Yang, T. (2024). Auditing Entertainment Traps on YouTube: How Do Recommendation Algorithms Pull Users Away from News. Political Communication, 41(6), 903–920.
    https://doi.org/10.1080/10584609.2024.2343769

    Khan, M.L. (2017). Social media engagement: What motivates user participation and consumption on YouTube? Computers in Human Behavior, 66, 236–247.
    https://doi.org/10.1016/j.chb.2016.09.024

    QR CODE
    :::