OPTIMIZING PROGRAMMATIC ADVERTISING: A MACHINE LEARNING APPROACH TO PREDICTIVE AD TARGETING

Authors

  • Ankush Singhal Software Development Manager, Amazon, USA Author

DOI:

https://doi.org/10.60087/jklst.vol3.n3.p.327-339

Abstract

In the ever-evolving landscape of digital advertising, programmatic advertising has emerged as a pivotal tool for automating ad placement and targeting audiences at scale. However, traditional methods often fall short in accurately predicting user behavior and delivering relevant ads to the right audiences. This study explores the potential of machine learning (ML) techniques to enhance predictive ad targeting within the programmatic advertising ecosystem. By applying a range of supervised and unsupervised ML models, including decision trees, neural networks, and clustering algorithms, we assess the ability of these models to improve ad relevance and engagement while optimizing budget allocation. Our findings reveal that ML-driven predictive targeting significantly increases click-through rates (CTR) and conversion rates compared to conventional targeting strategies. This research highlights the implications of using ML to improve ad targeting precision, reduce ad spend wastage, and enhance user experience. These insights contribute to the advancement of programmatic advertising strategies by demonstrating the transformative impact of AI and ML on audience segmentation and predictive ad delivery.

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References

Baker, H. K., Pandey, N., Kumar, S., & Haldar, A. (2020). A bibliometric analysis of board diversity: Current status, development, and future research directions. Journal of Business Research, 108, 232–246.

Bhatt, V. K. (2021, November 24–26). Assessing the significance and impact of artificial intelligence and machine learning in placement of advertisements [Conference session]. 2021 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), Marrakech, Morocco, 1–6. IEEE.

Campbell, C., Plangger, K., Sands, S., Kietzmann, J., & Bates, K. (2022). How deepfakes and artificial intelligence could reshape the advertising industry: The coming reality of AI fakes and their potential impact on consumer behavior. Journal of Advertising Research, 62(3), 241–251.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 1–47.

Gao, B. (2023). Understanding smart education continuance intention in a delayed benefit context: An integration of sensory stimuli, UTAUT, and flow theory. Acta Psychologica, 234, 103856.

Gao, B., & Huang, L. (2021). Toward a theory of smart media usage: The moderating role of smart media market development. Mathematical Biosciences and Engineering, 18(6), 7218–7238.

Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3(2022), 119–132.

Jaiwant, S. V. (2023). The changing role of marketing: Industry 5.0-the game changer. In A. Saini & V. Garg (Eds.), Transformation for sustainable business and management practices: Exploring the spectrum of industry 5.0 (pp. 187–202). Emerald Publishing Limited.

Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), 263–267.

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155.

Lai, Z. (2021). Research on advertising core business reformation driven by artificial intelligence. Journal of Physics: Conference Series, 1757(1), 012018.

Laux, J., Stephany, F., Russell, C., Wachter, S., & Mittelstadt, B. (2022). The Concentration-after-Personalisation Index (CAPI): Governing effects of personalisation using the example of targeted online advertising. Big Data & Society, 9(2), 1–15.

Malthouse, E., & Copulsky, J. (2023). Artificial intelligence ecosystems for marketing communications. International Journal of Advertising, 42(1), 128–140.

Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161, 1–23.

Mühlhoff, R., & Willem, T. (2023). Social media advertising for clinical studies: Ethical and data protection implications of online targeting. Big Data & Society, 10(1), 1–15.

Murgai, A. (2018). Transforming digital marketing with artificial intelligence. International Journal of Latest Technology in Engineering, Management & Applied Science, 7(4), 259–262.

Nair, K., & Gupta, R. (2021). Application of AI technology in modern digital marketing environment. World Journal of Entrepreneurship, Management and Sustainable Development, 17(3), 318–328.

Nikolajeva, A., & Teilans, A. (2021). Machine learning technology overview in terms of digital marketing and personalization. In L. Campanile & A. Bargiela (Eds.), ECMS (pp. 125–130). European Council for Modelling and Simulation (ECMS).

Pranckute, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 1–59.

Wiredu, J. (2023). An investigation on the characteristics, abilities, constraints, and functions of artificial intelligence (AI): The age of ChatGPT as an essential. Information and Management, 108(3), 62614–62620.

Zhang, Q., Lu, J., & Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems, 7, 439–457.

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Published

25-09-2024

How to Cite

Singhal, A. (2024). OPTIMIZING PROGRAMMATIC ADVERTISING: A MACHINE LEARNING APPROACH TO PREDICTIVE AD TARGETING. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 327-339. https://doi.org/10.60087/jklst.vol3.n3.p.327-339

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