Optimization of Marketing Strategies Employing LLMs: A Systematic Review

Optimization of Marketing Strategies Employing LLMs: A Systematic Review

Publicado 2024-08-02


https://doi.org/10.30554/lumina.v25.n2.5147.2024
Abrir | Descargar
PDF FLIP
Número
Vol. 25 Núm. 2 (2024)
Sección
Review article
Cómo citar
Saurith Moreno, J. G., Blanco Galan, D. C., Mindiola Garizado, S., & Ruiz Muñoz, J. F. (2024). Optimization of Marketing Strategies Employing LLMs: A Systematic Review. Lúmina, 25(2), E0058. https://doi.org/10.30554/lumina.v25.n2.5147.2024
Descargar cita
Endnote/Zotero/Mendeley (RIS) BibTeX
DOI

https://doi.org/10.30554/lumina.v25.n2.5147.2024
Dimensions
PlumX

Cómo citar

Saurith Moreno, J. G., Blanco Galan, D. C., Mindiola Garizado, S., & Ruiz Muñoz, J. F. (2024). Optimization of Marketing Strategies Employing LLMs: A Systematic Review. Lúmina, 25(2), E0058. https://doi.org/10.30554/lumina.v25.n2.5147.2024

Descargar cita

Licencia
Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Juan G. Saurith-Moreno
Universidad Nacional de Colombia – Sede de La Paz
Perfil Google Scholar
https://orcid.org/0009-0006-2900-5376 ORCID
Daniel Blanco-Galan
Universidad Nacional de Colombia – Sede de La Paz
Perfil Google Scholar
https://orcid.org/0009-0007-8578-8691 ORCID
Sebastian Mindiola G.
Universidad Nacional de Colombia – Sede de La Paz
Perfil Google Scholar
https://orcid.org/0009-0005-0651-2327 ORCID
Jose Francisco Ruiz-Munoz
Universidad Nacional de Colombia – Sede de La Paz
Perfil Google Scholar
https://orcid.org/0000-0002-0895-2036 ORCID
Mostrar biografía de los autores

This comprehensive systematic review delves into the use of Large Language Models (LLMs) in the field of digital marketing, focusing on their evolution, influence, and current practical applications. Employing a meticulous approach to analyze the scholarly literature in Scopus and Web of Science databases, this review ranges from basic implementations of LLMs to their more sophisticated applications in the marketing field. In particular, models such as ChatGPT have transformed the existing customer salesperson relationship, providing accurate and personalized real-time responses that have significantly improved the customer experience and the effectiveness of marketing strategies. The review focuses on three main areas: the integration of LLMs into chatbots, which has improved the user experience and boosted conversion and retention rates in e-commerce; adaptive content development to optimize social media advertising campaigns; and predictive analytics, which streamlines strategic decision-making by analyzing historical and real-time data. These advances have enabled marketers to design more informed and personalized strategies, refine campaigns and promotions, and improve the overall customer experience.

Visitas del artículo 568 | Visitas PDF 453

Descargas

Los datos de descarga todavía no están disponibles.
  1. Abdelkader, O. A. (2023). ChatGPT’s influence on customer experience in digital marketing: Investigating the moderating roles. Heliyon, 9(8), e18770. https://doi.org/10.1016/j.heliyon.2023.e18770
  2. Alabed, A., Javornik, A., Gregory-Smith, D., & Casey, R. (2024). More than just a chat: A taxonomy of consumers’ relationships with conversational AI agents and their well-being implications. European Journal of Marketing, 58(2), 373-409. https://doi.org/10.1108/EJM-01-2023-0037
  3. Aswani Thampi P.R & Dr. Ambeesh Mon.S. (2024). Digital marketing: Opportunities and challenges for businesses. EPRA International Journal of Economic and Business Review, 7-12. https://doi.org/10.36713/epra14584
  4. Balakrishnan, J., & Dwivedi, Y. K. (2021). Role of cognitive absorption in building user trust and experience. Psychology & Marketing, 38(4), 643-668. https://doi.org/10.1002/mar.21462
  5. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
  6. Botero, C. M., Milanes, C. B., & Robledo, S. (2023). 50 years of the Coastal Zone Management Act: The bibliometric influence of the first coastal management law on the world. Marine Policy, 150, 105548. https://doi.org/10.1016/j.marpol.2023.105548
  7. Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing (Seventh edition). Pearson. ISBN: 978-1-292-24162-3 (ePub). Harlow, England; New York.
  8. Chang, W., & Park, J. (2024). A comparative study on the effect of ChatGPT recommendation and AI recommender systems on the formation of a consideration set. Journal of Retailing and Consumer Services, 78, 103743. https://doi.org/10.1016/j.jretconser.2024.103743
  9. De-Frutos-Torres, B., Pastor-Rodríguez, A., & Martín-García, N. (2021). Consumo de las plataformas sociales en internet y escepticismo a la publicidad. El profesional de la información, e300204. https://doi.org/10.3145/epi.2021.mar.04
  10. Dogru, T., Line, N., Mody, M., Hanks, L., Abbott, J., Acikgoz, F., Assaf, A., Bakir,S., Berbekova, A., Bilgihan, A., Dalton, A., Erkmen, E., Geronasso, M., Gomez, D., Graves, S., Iskender, A., Ivanov, S., Kizildag, M., Lee, M., … Zhang, T. (2023). Generative artificial intelligence in the hospitality and tourism industry: Developing a framework for future research. Journal of Hospitality & Tourism Research, 10963480231188663. https://doi.org/10.1177/10963480231188663
  11. Dumitriu, D., & Popescu, M. A.-M. (2020). Artificial intelligence solutions for digital marketing. Procedia Manufacturing, 46, 630-636. https://doi.org/10.1016/j.promfg.2020.03.090
  12. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
  13. Ferrara, E. (2023). Should ChatGPT be biased? Challenges and risks of bias in large language models. First Monday. https://doi.org/10.5210/fm.v28i11.13346
  14. Gao, F., & Zhang, L. (2020). Application of artificial intelligence and big data technology in digital marketing. Proceedings of the 2020 2nd International Conference on Big Data and Artificial Intelligence, 270-272. https://doi.org/10.1145/3436286.3436404
  15. Gołąb-Andrzejak, E. (2023). The impact of generative ai and chatgpt on creating digital advertising campaigns. Cybernetics and Systems, 1-15. https://doi.org/10.1080/01969722.2023.2296253
  16. Gupta, P., Seetharaman, A., & Raj, J. R. (2013). The usage and adoption of cloud computing by small and medium businesses. International Journal of Information Management, 33(5), 861-874. https://doi.org/10.1016/j.ijinfomgt.2013.07.001
  17. Huang, S. Y. B., Lee, C. J., & Lee, S. C. (2021). Toward a unified theory of customer continuance model for financial technology chatbots. Sensors, 21(17), 5687. https://doi.org/10.3390/s21175687
  18. Ibáñez Vargas, C. A. (2024). La Aplicación de la Inteligencia Artificial en el Mercado Mexicano y sus Impactantes Beneficios en el Mercadeo. https://repository.usta.edu.co/handle/11634/54488
  19. Jenneboer, L., Herrando, C., & Constantinides, E. (2022). The impact of chatbots on customer loyalty: systematic literature review. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), 212-229. https://doi.org/10.3390/jtaer17010011
  20. Jha, S., Gupta, S., & Mahajan, R. (2023). The effect of motivated consumer innovativeness on the intention to use chatbots in the travel and tourism sector. Asia Pacific Journal of Tourism Research, 28(7), 729-744. https://doi.org/10.1080/10941665.2023.2264972
  21. Kaczorowska-Spychalska, D. (2019). How chatbots influence marketing. Management, 23(1), 251-270. https://doi.org/10.2478/manment-2019-0015
  22. Kamoonpuri, S. Z., & Sengar, A. (2023). Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail. Journal of Retailing and Consumer Services, 72, 103258. https://doi.org/10.1016/j.jretconser.2023.103258
  23. Kapoor, R., & Kapoor, K. (2021). The transition from traditional to digital marketing: A study of the evolution of e-marketing in the Indian hotel industry. Worldwide Hospitality and Tourism Themes, 13(2), 199-213. https://doi.org/10.1108/WHATT-10-2020-0124
  24. Kar, A. K., & Kushwaha, A. K. (2023). Facilitators and barriers of artificial intelligence adoption in business – insights from opinions using big data analytics. Information Systems Frontiers, 25(4), 1351-1374. https://doi.org/10.1007/s10796-021-10219-4
  25. Khoa, B. T. (2021). The impact of chatbots on the relationship between integrated marketing communication and online purchasing behavior in the frontier market. Jurnal The Messenger, 13(1), 19. https://doi.org/10.26623/themessenger.v13i1.2439
  26. Kim, T., Usman, U., Garvey, A., & Duhachek, A. (2023). Artificial intelligence in marketing and consumer behavior research. Foundations and Trends® in Marketing, 18(1), 1-93. https://doi.org/10.1561/1700000078
  27. Kim, W. B., & Hur, H. J. (2023). What makes people feel empathy for ai chatbots? Assessing the role of competence and warmth. International Journal of Human–Computer Interaction, 1-14. https://doi.org/10.1080/10447318.2023.2219961
  28. Kishore, R., Ansari, K., Kaur, J., Ahmad, A., Yadav, S., & Lakshendra, A. (2023). Assessing the role of digital marketing in navigating challenges in the indian business landscape. Journal of Informatics Education and Research, 3(2). https://doi.org/10.52783/jier.v3i2.375
  29. Labib, E. (2024). Artificial intelligence in marketing: Exploring current and future trends. Cogent Business & Management, 11(1), 2348728. https://doi.org/10.1080/23311975.2024.2348728
  30. Leeflang, P. S. H., Verhoef, P. C., Dahlström, P., & Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1-12. https://doi.org/10.1016/j.emj.2013.12.001
  31. Li, C. Y., Fang, Y. H., & Chiang, Y. H. (2023). Can AI chatbots help retain customers? An integrative perspective using affordance theory and servicedomain logic. Technological Forecasting and Social Change, 197, 122921. https://doi.org/10.1016/j.techfore.2023.122921
  32. Lim, W. M. (2015). The influence of internet advertising and electronic word of mouth on consumer perceptions and intention: Some evidence from online group buying. Journal of Computer Information Systems, 55(4), 81-89. https://doi.org/10.1080/08874417.2015.11645790
  33. Liu, D., Lv, Y., & Huang, W. (2023). How do consumers react to chatbots’ humorous emojis in service failures. Technology in Society, 73, 102244. https://doi.org/10.1016/j.techsoc.2023.102244
  34. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. Humans: the impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, mksc.2019.1192. https://doi.org/10.1287/mksc.2019.1192
  35. Marjerison, R. K., Zhang, Y., & Zheng, H. (2022). Ai in e-commerce: Application of the use and gratification model to the acceptance of chatbots. Sustainability, 14(21), 14270. https://doi.org/10.3390/su142114270
  36. Mogaji, E., Soetan, T. O., & Kieu, T. A. (2021). The implications of artificial intelligence on the digital marketing of financial services to vulnerable customers. Australasian Marketing Journal, 29(3), 235-242. https://doi.org/10.1016/j.ausmj.2020.05.003
  37. Ooi, K.-B., Tan, G. W.-H., Al-Emran, M., Al-Sharafi, M. A., Capatina, A., Chakraborty, A., Dwivedi, Y. K., Huang, T.-L., Kar, A. K., Lee, V.-H., Loh, X.-M., Micu, A., Mikalef, P., Mogaji, E., Pandey, N., Raman, R., Rana, N. P., Sarker, P., Sharma, A., … Wong, L.-W. (2023). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 1-32. https://doi.org/10.1080/08874417.2023.2261010
  38. Peltier, J. W., Dahl, A. J., & Schibrowsky, J. A. (2024). Artificial intelligence in interactive marketing: A conceptual framework and research agenda. Journal of Research in Interactive Marketing, 18(1), 54-90. https://doi.org/10.1108/JRIM-01-2023-0030
  39. Pizzi, G., Vannucci, V., Mazzoli, V., & Donvito, R. (2023). I, chatbot! The impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentions. Psychology & Marketing, 40(7), 1372-1387. https://doi.org/10.1002/mar.21813
  40. Priyanga, G. (2023). The effects of artificial intelligence on digital marketing. ShodhKosh: Journal of Visual and Performing Arts, 4(1SE). https://doi.org/10.29121/shodhkosh.v4.i1SE.2023.431
  41. Robledo, S., Vasquez, J. E., Duque-Méndez, N. D., & Duque-Uribe, V. (2023). Networking as an entrepreneurial marketing tool: The link between effectuation and word of mouth. Journal of Research in Marketing and Entrepreneurship, 25(2), 270-285. https://doi.org/10.1108/JRME-08-2020-0112
  42. Rozado, D. (2023). The political biases of chatgpt. Social Sciences, 12(3), 148. https://doi.org/10.3390/socsci12030148
  43. Ruiz, G. A., Robledo Giraldo, S., & Hernando Morales, H. (2023). Static code analysis: A tree of science review. Entre ciencia e ingeniería, 17(34), 9-14. https://doi.org/10.31908/19098367.2846
  44. Rutinowski, J., Franke, S., Endendyk, J., Dormuth, I., Roidl, M., & Pauly, M. (2024). The self-perception and political biases of chatgpt. Human Behavior and Emerging Technologies, 2024, 1-9. https://doi.org/10.1155/2024/7115633
  45. Salminen, J., Kandpal, C., Kamel, A. M., Jung, S., & Jansen, B. J. (2022). Creating and detecting fake reviews of online products. Journal of Retailing and Consumer Services, 64, 102771. https://doi.org/10.1016/j.jretconser.2021.102771
  46. Selamat, M. A., & Windasari, N. A. (2021). Chatbot for SMEs: Integrating customer and business owner perspectives. Technology in Society, 66, 101685. https://doi.org/10.1016/j.techsoc.2021.101685
  47. Singh, A., Sharma, V., Pandey, S., Gehlot, A., Gusain, A., & Das, P. (2023). Effectiveness of digital marketing to protect customer’s through artificial intelligence. 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), 222-226. https://doi.org/10.1109/CISES58720.2023.10183618
  48. Song, M., Zhang, H., Xing, X., & Duan, Y. (2023). Appreciation vs. apology: Research on the influence mechanism of chatbot service recovery based on politeness theory. Journal of Retailing and Consumer Services, 73, 103323. https://doi.org/10.1016/j.jretconser.2023.103323
  49. Tafesse, W., & Wood, B. (2024). Hey ChatGPT: An examination of ChatGPT prompts in marketing. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-023-00284-w
  50. Tanwar, M., & Verma, H. V. (2024). Scientific mapping of chatbot literature: A bibliometric analysis. International Journal of Mathematical, Engineering and Management Sciences, 9(2), 323-340. https://doi.org/10.33889/IJMEMS.2024.9.2.017
  51. Tene, O., & Polonetsky, J. (2013). Big data for all: Privacy and user control in the age of analytics. Northwestern Journal of Technology and Intellectual Property, 11(5), 239. https://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1
  52. Theodoridis, P. K., & Gkikas, D. C. (2019). How artificial intelligence affects digital marketing. En A. Kavoura, E. Kefallonitis, & A. Giovanis (Eds.), Strategic Innovative Marketing and Tourism (pp. 1319-1327). Springer International Publishing. https://doi.org/10.1007/978-3-030-12453-3_151
  53. Van Den Broeck, E., Zarouali, B., & Poels, K. (2019). Chatbot advertising effectiveness: When does the message get through? Computers in Human Behavior, 98, 150-157. https://doi.org/10.1016/j.chb.2019.04.009
  54. Van Esch, P., & Stewart Black, J. (2021). Artificial intelligence (Ai): Revolutionizing digital marketing. Australasian Marketing Journal, 29(3), 199-203. https://doi.org/10.1177/18393349211037684
  55. Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (Ai) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
  56. Wang, X., Lin, X., & Shao, B. (2022). How does artificial intelligence create business agility? Evidence from chatbots. International Journal of Information Management, 66, 102535. https://doi.org/10.1016/j.ijinfomgt.2022.102535
  57. Xu, Z., Zhou, F., & Zhou, J. (2021). Sentiments and real business cycles. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3932016
  58. Youn, S., & Jin, S. V. (2021). “In A.I. we trust?” The effects of parasocial interaction and technopian versus luddite ideological views on chatbot-based customer relationship management in the emerging “feeling economy”. Computers in Human Behavior, 119, 106721. https://doi.org/10.1016/j.chb.2021.106721
  59. Zhang, W., & Agnihotri, R. (2024). Artificial intelligence for business and marketing: A customer-centric approach. Journal of Marketing Theory and Practice, 1-5. https://doi.org/10.1080/10696679.2024.2332692
  60. Zheng, S., Yahya, Z., Wang, L., Zhang, R., & Hoshyar, A. N. (2023). Multiheaded deep learning chatbot for increasing production and marketing. Information Processing & Management, 60(5), 103446. https://doi.org/10.1016/j.ipm.2023.103446
  61. Zhou, W., Zhang, C., Wu, L., & Shashidhar, M. (2023). ChatGPT and marketing: Analyzing public discourse in early Twitter posts. Journal of Marketing Analytics, 11(4), 693-706. https://doi.org/10.1057/s41270-023-00250-6
  62. Zhu, Y., Zhang, R. (Renata), Zou, Y., & Jin, D. (2023). Investigating customers’ responses to artificial intelligence chatbots in online travel agencies: The moderating role of product familiarity. Journal of Hospitality and Tourism Technology, 14(2), 208-224. https://doi.org/10.1108/JHTT-02-2022-0041

Información

Perfil de Google Scholar

Indexación
DIRECTORIOS

PKP INDEX

LATIN REV

CAPES

BASE

OAJI

ECONBIZ

MIAR

BASE

SCILIT         

WORLDCAT

CROSSREF 

CIRC

EZB LIBRARY

 

Palabras clave

Código QR

Sistema OJS 3.4.0.10 - Metabiblioteca |