Navigating the New Era of SEO: Voice Search, AI, and User Intent

Lintang Anis Bena Kinanti, Pramelani Pramelani, I Gede Novian Suteja

Sari


The evolution of search engine optimization (SEO) in the digital era has been significantly influenced by the rise of artificial intelligence (AI), the increasing adoption of voice search technologies, and the growing emphasis on understanding user intent. This study explores how SEO professionals adapt to these transformations through a qualitative research design involving in-depth interviews with industry practitioners. Findings reveal that traditional keyword-based strategies are being replaced by AI-driven, intent-focused, and conversational content approaches. Key themes include the restructuring of content for voice search, the use of predictive analytics for intent modeling, and the integration of AI tools for content customization. Participants also identified critical challenges such as algorithm transparency, data privacy limitations, and the need to maintain human oversight in automated systems. The study concludes that the future of SEO lies in a balanced integration of AI capabilities and human strategic thinking, where adaptability, contextual relevance, and ethical considerations define successful digital visibility in an increasingly intelligent search environment.

 

Keywords: Search Engine Optimization, Voice Search, Artificial Intelligence, User Intent, Digital Marketing, Content Strategy  

 


Teks Lengkap:

PDF

Referensi


Almeida, F., Santos, J. D., & Monteiro, J. A. (2021). The influence of voice search on digital marketing: Trends and challenges. Journal of Business Research, 129, 902–910. https://doi.org/10.1016/j.jbusres.2020.11.044

Arguello, J., Elsayed, T., & Lin, J. (2021). Understanding searcher behavior with deep learning models. Information Processing & Management, 58(3), 102494. https://doi.org/10.1016/j.ipm.2021.102494

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

Brinkmann, S. (2018). The interview: A philosophical approach. In N. K. Denzin & Y. S. Lincoln (Eds.), The SAGE handbook of qualitative research (5th ed., pp. 997–1032). SAGE Publications.

Chaffey, D., & Smith, P. R. (2021). Digital marketing excellence: Planning, optimizing and integrating online marketing (6th ed.). Routledge.

Chia, A., & Lim, W. (2021). Conversational AI in marketing: Ethical implications and strategic considerations. Technological Forecasting and Social Change, 172, 121013. https://doi.org/10.1016/j.techfore.2021.121013

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT, 4171–4186. https://aclanthology.org/N19-1423/

Fernández-Tobías, I., Cantador, I., & Bellogín, A. (2020). A comparative analysis of collaborative filtering algorithms for personalized search. ACM Transactions on the Web (TWEB), 14(2), 1–36. https://doi.org/10.1145/3382037

Jansen, B. J., & Schuster, S. (2019). Bidding on the buying funnel for sponsored search and keyword advertising. Journal of the Association for Information Science and Technology, 70(4), 340–357. https://doi.org/10.1002/asi.24112

Jarek, K., & Mazurek, G. (2019). Marketing and artificial intelligence. Central European Business Review, 8(2), 46–55. https://doi.org/10.18267/j.cebr.213

Kietzmann, J., Paschen, J., & Treen, E. (2021). Artificial intelligence in advertising: How marketers can leverage AI to improve customer relationships. Journal of Advertising Research, 61(2), 215–227. https://doi.org/10.2501/JAR-2021-011

Li, J., & Song, J. (2022). Predicting user intent in conversational systems with deep learning: A survey. Neural Networks, 148, 162–180. https://doi.org/10.1016/j.neunet.2022.01.018

Liao, Q. V., Gruen, D. M., & Miller, S. (2020). Questioning the AI: Informing design practices for explainable AI user experiences. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–15. https://doi.org/10.1145/3313831.3376590

Liu, Y., & Singh, P. (2020). AI-powered automation in digital marketing: Challenges and future directions. Information Systems Frontiers, 22(5), 1091–1105. https://doi.org/10.1007/s10796-019-09923-6

McCormick, K., Srivastava, M., & Vassileva, J. (2022). Modeling consumer behavior with AI for personalized content delivery. Journal of Consumer Behaviour, 21(1), 13–29. https://doi.org/10.1002/cb.1941

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13. https://doi.org/10.1177/1609406917733847

Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y

Pappas, I. O., Patelis, T. E., & Giannakos, M. N. (2020). Voice interaction in digital experiences: A synthesis of consumer behavior. Computers in Human Behavior, 112, 106456. https://doi.org/10.1016/j.chb.2020.106456

Ribeiro, M., Monteiro, J., & Almeida, F. (2022). SEO in the age of artificial intelligence: Systematic review and research agenda. Technological Forecasting and Social Change, 176, 121476. https://doi.org/10.1016/j.techfore.2021.121476

Singhal, A., & Kanungo, T. (2020). User modeling in web search: Recent advances and emerging trends. IEEE Internet Computing, 24(5), 40–50. https://doi.org/10.1109/MIC.2020.2996729

Van Dijk, J., & Poell, T. (2021). Social media platforms and the logic of algorithms. New Media & Society, 23(7), 1789–1806. https://doi.org/10.1177/1461444820982785

Yao, S., Liu, B., & Jin, Y. (2021). User intent classification in search engines using deep learning techniques. IEEE Access, 9, 61854–61864. https://doi.org/10.1109/ACCESS.2021.3074765

Zhou, H., Xu, Y., & Wang, J. (2021). Voice search optimization and content strategy for AI-driven user experience. Information & Management, 58(6), 103521. https://doi.org/10.1016/j.im.2021.103521




DOI: https://doi.org/10.37531/yum.v8i2.9162

Refbacks

  • Saat ini tidak ada refbacks.


Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional
Web Analytics Made Easy - StatCounter