Designing a Model for Sustainable YouTube Channel Growth
DOI:
https://doi.org/10.70862/CSIR.2026.0301-20Keywords:
Audience analysis, Content optimization, YouTube analytics, YouTube recommendation algorithm, YouTube channel developmentAbstract
This study examines the problem of the lack of an integrated model for the sustainable development of a YouTube channel tailored to a specific target audience. Under conditions of algorithmically driven visibility on the YouTube platform, content creators are faced with the need to combine analytical, content-related, and managerial factors within a unified strategic framework. The article aims to develop a practically applicable model that integrates audience analysis, content optimization, video structuring, SEO practices, and systematic analysis of data from YouTube Analytics. Methodologically, the study combines an analysis of scholarly literature, an analysis of active YouTube channels, the dynamics of channel development, and the interpretation of behavioral metrics. The proposed framework is designed to support the improvement of key behavioral indicators such as watch time, audience retention, and click-through rate by aligning content strategy with audience behavior and platform recommendation dynamics. In this sense, the model provides a structured basis for sustainable organic channel development.
References
Bryant, L. V. (2020). The YouTube algorithm and the alt-right filter bubble. Open Information Science, 4(1), 85–90. https://doi.org/10.1515/opis-2020-0007
Shen, J. (2024). The research of the factors that influence the popularity of YouTube videos. Theoretical and Natural Science, 51(1), 187–193. https://doi.org/10.54254/2753-8818/51/2024CH0200
Goodrow, C. (2021, September 15). On YouTube’s recommendation system. YouTube. https://blog.youtube/inside-youtube/on-youtubes-recommendation-system
YouTube. (n.d.). How YouTube recommendations work. Retrieved June 2, 2024, from https://support.google.com/youtube/answer/6342839
Chen, L. (2023). Effective strategies for attracting and engaging target audiences in today's competitive market. Journal of Education, Humanities and Social Sciences, 16, 141–147. https://doi.org/10.54097/ehss.v16i.9587
YouTube. (n.d.). Get started with YouTube Analytics. Retrieved June 2, 2024, from https://support.google.com/youtube/answer/9002587
Yu, X., Haroon, M., Menchen-Trevino, E., & Wojcieszak, M. (2024). Nudging recommendation algorithms increases news consumption and diversity on YouTube. PNAS Nexus, 3(12), pgae518. https://doi.org/10.1093/pnasnexus/pgae518
Yang, S., Brossard, D., Scheufele, D. A., & Xenos, M. A. (2022). The science of YouTube: What factors influence user engagement with online science videos? PLOS ONE, 17(5), e0267697. https://doi.org/10.1371/journal.pone.0267697
Violot, C., Elmas, T., Bilogrevic, I., & Humbert, M. (2024). Shorts vs. regular videos on YouTube: A comparative analysis of user engagement and content creation trends. In Proceedings of the 16th ACM Web Science Conference (pp. 213–223). https://doi.org/10.1145/3614419.3644023
Dai, X., & Wang, J. (2023). Effect of online video infotainment on audience attention. Humanities and Social Sciences Communications, 10(1), 421. https://doi.org/10.1057/s41599-023-01921-6
Buffer. (2025). Best time to post on YouTube in 2025. Retrieved April 12, 2026, from https://buffer.com/resources/best-time-to-post-on-youtube
Nugent, R. (2011, August 27). YouTube Creator Playbook tips: The first 15 seconds. YouTube. https://blog.youtube/creator-and-artist-stories/youtube-creator-playbook-tips-first-15
YouTube. (n.d.). YouTube’s recommendation system. Retrieved April 12, 2026, from https://support.google.com/youtube/answer/6342839
Meyerson, E. (2012, August 10). YouTube now: Why we focus on watch time. https://blog.youtube/news-and-events/youtube-now-why-we-focus-on-watch-time/
Poudel, D., Cakmak, M. C., & Agarwal, N. (2024). Beyond the click: How YouTube thumbnails shape user interaction and algorithmic recommendations. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (pp. 181–196). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-85240-4_15
Song, Y., Redi, M., Vallmitjana, J., & Jaimes, A. (2016). To click or not to click: Automatic selection of beautiful thumbnails from videos. arXiv preprint arXiv:1609.01388. https://doi.org/10.48550/arXiv.1609.01388
YouTube. (n.d.). Understand your YouTube video reach. Retrieved April 12, 2026, from https://support.google.com/youtube/answer/9314355
Backlinko. (2024). Audience retention. https://backlinko.com/hub/youtube/retention
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Copyright (c) 2026 Lachezar Gushkov, Simeon Kondov, Hristo Hristov
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
