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A COMPARATIVE EXPERIMENTAL STUDY ON ARTIFICIAL INTELLIGENCE- AND HUMAN-DRIVEN SOCIAL MEDIA MARKETING CAMPAIGNS

Year 2024, Volume: 11 Issue: 2, 92 - 100, 31.12.2024

Abstract

Purpose- The rapid advancement in digital marketing, driven by technologies such as artificial intelligence (AI), forms the backdrop for this research. This study aims to investigate the performance differences between AI-driven and human-managed digital marketing campaigns by means of a true field experiment. Selected Key Performance Indicators (KPIs) are evaluated on the Meta platform to make a statement regarding the performance.
Methodology- The study has an experimantal research method. Two concurrent marketing campaigns for the Paul Kenzie brand were conducted over a two-week period: one fully created by ChatGPT-4 and the other by a human expert. Key KPIs measured include Click-Through Rate (CTR), number of conversions, conversion rate, and Return on Advertising Spend (ROAS).
Findings- The results indicate that AI-driven campaigns outperform human-managed campaigns in terms of CTR, conversion rate, and ROAS, suggesting higher efficiency and effectiveness in reaching and engaging the target audience.
Conclusion- The findings highlight the potential of integrating AI technologies with human creativity to optimize digital marketing strategies.

References

  • Al Adwan, A., Kokash, H., Al Adwan, R., & Khattak, A. (2023). Data analytics in digital marketing for tracking the effectiveness of campaigns and inform strategy. International Journal of Data and Network Science, 7, 563-573.
  • Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34(7), 1177-1190.
  • Alqurashi, D. R., Alkhaffaf, M., Daoud, M. K., Al-Gasawneh, J. A., & Alghizzawi, M. (2023). Exploring the impact of artificial intelligence in personalized content marketing: a contemporary digital marketing. Migration Letters, 20(S8), 548-560.
  • Ananthakrishnan, R., & Arunachalam, T. (2022). Comparison of consumers perception between human generated and AI aided brand content. Webology (ISSN: 1735-188X), 19(2).
  • Babatunde, S. O., Odejide, O. A., Edunjobi, T. E., & Ogundipe, D. O. (2024). The role of AI in marketing personalization: A theoretical exploration of consumer engagement strategies. International Journal of Management & Entrepreneurship Research, 6(3), 936-949.
  • Basal, M., Saraç, E., & Kadir, Ö. (2024). Dynamic pricing strategies using artificial ıntelligence algorithm. Open Journal of Applied Sciences, 14(8), 1963-1978.
  • Berman, R. (2019). Machine learning applications in digital marketing. International Journal of Marketing, 47(2), 210-225.
  • Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. V. (2013). Digital business strategy: toward a next generation of insights. MIS Quarterly, 471-482.
  • Bohndel, M., Jastorff, M., & Rudeloff, C. (2023). AI-driven influencer marketing: Comparing the effects of virtual and human influencers on consumer perceptions. Journal of AI, Robotics & Workplace Automation, 2(2), 165-174.
  • Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing: Strategy, implementation and practice. Pearson.
  • Chaffey, D., & Smith, P. R. (2022). Digital marketing excellence: planning, optimizing and integrating online marketing. Routledge.
  • Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38-68.
  • Choi, J. A., & Lim, K. (2020). Identifying machine learning techniques for classification of target advertising. ICT Express, 6(3), 175-180.
  • Constantinides, E. (2014). Foundations of social media marketing. Procedia-Social and Behavioral Sciences, 148, 40-57.
  • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42.
  • Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663-680.
  • Hartmann, J., Exner, Y., & Domdey, S. (2024). The power of generative marketing: Can generative AI create superhuman visual marketing content? SSRN Electronic Journal, 1-43.
  • Jarek, K., & Mazurek, G. (2019). Marketing and artificial intelligence. Central European Business Review, 8(2), 46-55.
  • Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25.
  • Kotler, P., Kartajaya, H., & Setiawan, I. (2016). Marketing 4.0: Moving from traditional to digital. Wiley.
  • Lammenett, E. (2024). Meta: Facebook und Instagram: Werbung in Facebook und Instagram, Werbung mit Facebook, Custom Audience, Facebook Business Manager. In Praxiswissen Online-Marketing. Wiesbaden: Springer Fachmedien Wiesbaden, 433-453.
  • Looi, J., & Kahlor, L. A. (2024). Artificial intelligence in influencer marketing: A mixed-method comparison of human and virtual influencers on Instagram. Journal of Interactive Advertising, 24(2), 107-126.
  • Malhotra, N. K., Nunan, D., & Birks, D. F. (2017). Marketing research: An applied approach. Pearson.
  • Malthouse, E. C., Haenlein, M., Skiera, B., Wege, E., & Zhang, M. (2013). Managing customer relationships in the social media era: Introducing the social CRM house. Journal of interactive marketing, 27(4), 270-280.
  • Mani, A. (2024). Applying artificial ıntelligence to the digital marketing: Opportunities and challenges for the marketer. International Journal of Business and Management Review, 12(1), 56-74.
  • Nanayakkara, N. (2020). Application of artificial ıntelligence in marketing mix: A conceptual review. In Proceedings of 11th International Conference on Business & Information ICBI, University of Kelaniya, Sri Lanka. ISSN 2465-6399, 530-542.
  • Olujimi, P. A., & Ade-Ibijola, A. (2023). NLP techniques for automating responses to customer queries: a systematic review. Discover Artificial Intelligence, 3(1), 20.
  • Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., & Provost, F. (2014). Machine learning for targeted display advertising: Transfer learning in action. Machine Learning, 95(1), 103-127.
  • Ryan, D. (2016). Understanding digital marketing: Marketing strategies for engaging the digital generation. Kogan Page Publishers.
  • Saputra, R., Nasution, M. I. P., & Dharma, B. (2023). The impact of using ai chat gpt on marketing effectiveness: A case study on Instagram marketing. Indonesian Journal of Economics and Management, 3(3), 603-617.
  • Saura, J. R., Palos-Sánchez, P., & Cerdá Suárez, L. M. (2017). Understanding the digital marketing environment with KPIs and web analytics. Future Internet, 9(4), 76.
  • Smith, A. (2018). Artificial intelligence and personalization in marketing. Journal of Marketing Research, 55(3), 435-450.
  • Spilski, A., Gröppel-Klein, A., & Gierl, H. (2018). Avoiding pitfalls in experimental research in marketing. Marketing: ZFP–Journal of Research and Management, 40(2), 58-90.
  • Statista. (2023). Digital advertising worldwide. https://www.statista.com/statistics/237974/online-advertising-spending-worldwide/ [Date Accessed: August 29, 2024].
  • Tauheed, J., Shabbir, A., & Pervez, M. S. (2024). Exploring the role of artificial intelligence in digital marketing strategies. Journal of Business, Communication & Technology, 54-65.
  • Tuten, T. L., & Solomon, M. R. (2017). Social media marketing. Sage Publications.
  • Ullal, M. S., Hawaldar, I. T., Mendon, S., & Nympha, R. J. (2020). The effect of artificial intelligence on the sales graph in Indian market. Entrepreneurship and Sustainability Issues, 7(4), 2940.
  • Viglia, G., Zaefarian, G., & Ulqinaku, A. (2021). How to design good experiments in marketing: Types, examples, and methods. Industrial Marketing Management, 98, 193-206.
  • Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.
There are 39 citations in total.

Details

Primary Language English
Subjects Integrated Marketing Communication, Human Resources and Industrial Relations (Other), Business Administration, Advertisement
Journal Section Articles
Authors

Müge Klein 0000-0003-2341-2975

Mehmet Fatih Kutlar 0009-0004-2168-8075

Publication Date December 31, 2024
Submission Date October 11, 2024
Acceptance Date December 11, 2024
Published in Issue Year 2024 Volume: 11 Issue: 2

Cite

APA Klein, M., & Kutlar, M. F. (2024). A COMPARATIVE EXPERIMENTAL STUDY ON ARTIFICIAL INTELLIGENCE- AND HUMAN-DRIVEN SOCIAL MEDIA MARKETING CAMPAIGNS. Journal of Management Marketing and Logistics, 11(2), 92-100. https://doi.org/10.17261/Pressacademia.2024.1935

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