Voice Search SEO: Optimizing Marketing Strategies for the Future

Authors

  • Santo Dewatmoko Sekolah Tinggi Ilmu Administrasi Bagasasi
  • Nia Sonani Universitas Nusa Bangsa
  • Angga Pramadista Sudrajat Universitas Linggabuana PGRI Sukabumi

DOI:

https://doi.org/10.37385/msej.v5i2.4930

Keywords:

: Voice Search SEO Optimization, Consumer Behavior Change, Natural Language Processing Technology, Marketing Strategy Success.

Abstract

This research examines the relationship between Voice Search SEO optimization, Consumer Behavior Change, Natural Language Processing Technology, and Marketing Strategy Success in the context of PT. Yakult Indonesia Persada - Bandung 3. Using a quantitative research design with random sampling, the study collects data from 100 consumers to explore both direct and indirect effects among these variables. The analysis, conducted with SmartPLS, reveals significant direct effects from Voice Search SEO optimization to Marketing Strategy Success and Consumer Behavior Change to Marketing Strategy Success, indicating that voice search optimization and shifts in consumer behavior play crucial roles in marketing strategy success. Additionally, the results show that Natural Language Processing Technology mediates the relationships between Voice Search SEO optimization and Marketing Strategy Success, and Consumer Behavior Change and Marketing Strategy Success, suggesting that advanced Natural Language Processing Technology is instrumental in translating voice-based optimization and evolving consumer behaviors into successful marketing outcomes. The findings highlight the importance of adapting marketing strategies to align with voice search trends and evolving consumer preferences. Companies that invest in voice search SEO and Natural Language Processing Technology  are better positioned to enhance customer experiences and achieve marketing success in a rapidly changing digital landscape. This research provides valuable insights for businesses seeking to remain competitive and meet the demands of a voice-driven consumer market.

References

Ahn, H. (2023). Unrevealing Voice Search Behaviors: Technology Acceptance Model Meets Anthropomorphism in Understanding Consumer Psychology in the U.S. Market. Sustainability, 15(23), 16455. https://doi.org/10.3390/su152316455

Feruza, O. (2023). Vital Annex?: International Journal of Novel Research in Advanced Sciences ( IJNRAS ) How to Create Effective Marketing Strategies for Your Business Vital Annex?: International Journal of Novel Research in Advanced Sciences ( IJNRAS ). International Journal of Novel Research in Advanced Sciences, 02(March), 12–17.

Gokkoeva, M. (2020). Trends in Search Engine Optimisation: The Role of Voice Search. Thesis.

Hadi, R., Melumad, S., & Park, E. S. (2024). The Metaverse: A new digital frontier for consumer behavior. Journal of Consumer Psychology, 34(1), 142–166. https://doi.org/10.1002/jcpy.1356

Hernandez, E., Schwettmann, S., Bau, D., Bagashvili, T., Torralba, A., & Andreas, J. (2022). GPT-4: A Review On Advancements And Opportunities In Natural Language Processing. 2, 1–21.

Hou, Y., & Poliquin, C. W. (2023). The effects of CEO activism: Partisan consumer behavior and its duration. Strategic Management Journal, 44(3), 672–703. https://doi.org/10.1002/smj.3451

Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4

Lopezosa, C., Guallar, J., Codina, L., & Pérez-Montoro, M. (2023). Voice search optimization in digital media: challenges, use and training | Optimización de búsquedas por voz en medios digitales: desafíos, uso y formación. Profesional de La Informacion, 32(3), 1–13.

Lozeva-Koleva, V., & Kolev, G. (2023). Voice search analysis in search engine optimization. Industry 4.0, 8(2), 36–38. https://stumejournals.com/journals/i4/2023/2/36

Luenberger, D. G. (2012). Information Science. In Information Science. https://doi.org/10.36311/1981-1640.2007.v1n1.03.p33

Makrydakis, N. (2024). SEO mix 6 O’s model and categorization of search engine marketing factors for websites ranking on search engine result pages. International Journal of Research in Marketing Management and Sales, 6(1), 18–32. https://doi.org/10.33545/26633329.2024.v6.i1a.146

Mishra, S., & Ashfaq, R. (2023). Influencer Impact: Examining the Effect of Influencers on Consumer Behavior and Purchase under the Creative Common Attribution Non-Commercial 4.0. Traditional Journal of Multidisciplinary Sciences (TJMS), 01(01), 55–72. https://ojs.traditionaljournaloflaw.com/index.php/TJMS

Mladenovi?, D., Rajapakse, A., Kožuljevi?, N., & Shukla, Y. (2023). Search engine optimization (SEO) for digital marketers: exploring determinants of online search visibility for blood bank service. Online Information Review, 47(4), 661–679. https://doi.org/10.1108/OIR-05-2022-0276

Pham, H. N., Thai, N. T., Heffernan, T. W., & Reynolds, N. (2024). Environmental Policies and the Promotion of Pro-Environmental Consumer Behavior: A Systematic Literature Review. Journal of Macromarketing, 44(1), 30–58. https://doi.org/10.1177/02761467231201507

Phatthiyaphaibun, W., Chaovavanich, K., Polpanumas, C., Suriyawongkul, A., Lowphansirikul, L., Chormai, P., Limkonchotiwat, P., Suntorntip, T., & Udomcharoenchaikit, C. (2023). PyThaiNLP: Thai Natural Language Processing in Python. 3rd Workshop for Natural Language Processing Open Source Software, NLP-OSS 2023, Proceedings of the Workshop, 25–36. https://doi.org/10.18653/v1/2023.nlposs-1.4

Runaite, D. (2021). How will voice search optimisation aid or limit digital marketing? An End-User Perspective. National College of Ireland, August.

Saab, J. (2023). The Impact of Artificial Intelligence on Search Engine. ICEBSS, 141–160. https://doi.org/10.4018/978-1-6684-6937-8.ch007

Saura, J. R., Palacios-Marqués, D., & Barbosa, B. (2023). A review of digital family businesses: setting marketing strategies, business models and technology applications. International Journal of Entrepreneurial Behaviour and Research, 29(1), 144–165. https://doi.org/10.1108/IJEBR-03-2022-0228

Šostar, M., & Ristanovi?, V. (2023). Assessment of Influencing Factors on Consumer Behavior Using the AHP Model. Sustainability (Switzerland), 15(13). https://doi.org/10.3390/su151310341

Sudirjo, F. (2023). Marketing Strategy in Improving Product Competitiveness in the Global Market. Journal of Contemporary Administration and Management (ADMAN), 1(2), 63–69. https://doi.org/10.61100/adman.v1i2.24

Tyagi, N., & Bhushan, B. (2023). Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions. Wireless Personal Communications, 857–908. https://doi.org/10.1007/s11277-023-10312-8

Xu, X., Xu, Z., Ling, Z., Jin, Z., Du, S., Researcher, I., Science, C., & Studies, I. (2021). Comprehensive Implementation of TextCNN for Enhanced Collaboration between Natural Language Processing and System Recommendation. SPIE Proceedings Publications Comprehensive, 1–12.

Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18(May), 200235. https://doi.org/10.1016/j.iswa.2023.200235

Downloads

Published

2024-04-28

How to Cite

Dewatmoko, S., Sonani, N., & Sudrajat, A. P. . (2024). Voice Search SEO: Optimizing Marketing Strategies for the Future . Management Studies and Entrepreneurship Journal (MSEJ), 5(2), 5054–5061. https://doi.org/10.37385/msej.v5i2.4930