AI-DRIVEN OPTIMISATION OF DIGITAL ADVERTISING CAMPAIGNS: A PERFORMANCE-BASED APPROACH
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The rapid advancement of artificial intelligence (AI) has transformed the digital marketing landscape, particularly with regard to optimising online advertising campaigns. This study explores how AI-driven techniques can enhance the performance and efficiency of digital advertising. It aims to evaluate the impact of machine learning algorithms and automated bidding systems on key performance indicators, such as click-through rate (CTR), conversion rate and return on investment (ROI). The study takes a quantitative research approach, using comparative analysis to compare AI-optimised advertising campaigns with traditionally managed ones across major platforms such as Google Ads and Meta Ads. Data from multiple campaigns is collected over a defined period and analysed using statistical methods, including regression analysis and hypothesis testing. The findings suggest that AI-driven optimisation can greatly enhance campaign performance by facilitating real-time decision-making, precise audience targeting and dynamic budget allocation. Furthermore, the results show that automated systems are more efficient and scalable than manual campaign management. This research makes a valuable contribution to the growing body of literature on AI in digital marketing, providing empirical evidence of the effectiveness of intelligent optimisation techniques. The study also offers marketers practical insights on how to enhance advertising outcomes by integrating AI technologies. Proposed areas for future research include exploring ethical considerations and the long-term sustainability of AI-driven marketing strategies.
How to Cite
##plugins.themes.bootstrap3.article.details##
artificial intelligence, digital advertising, campaign optimisation, machine learning, programmatic advertising
Antonova, D., Beloeva, S., & Todorova, A. (2024). The dual impact of artificial intelligence: catalyst for innovation or threat to stability. Strategies for policy in science and education, 32 (6s), 49–58. https://doi.org/10.53656/str2024-6s-4-dua
Azimov, D., Stoyanova-Asenova, S., Petrova, M., & Asenov, A. (2024). Multi-Criteria Analysis Techniques to Assess the Efficiency of Using Blockchain in Logistics. Economics Ecology Socium, 8, 1–11. https://doi.org/10.61954/2616-7107/2024.8.2-1
Beloeva, S., Venelinova, N., & Petrova, M. (2025). Building competencies for managing virtual teams in local public administrations: challenges in the age of global digitalization. Baltic Journal of Economic Studies, 11(4), 252–259. https://doi.org/10.30525/2256-0742/2025-11-4-252-259
Beloeva, S., Levakov, I., Venelinova, N., Akhmedov, A., & Makhmudov, M. (2026). AI-Driven Remarketing and Digital Infrastructure in Emerging Markets: Evidence from Tourism and Textile Enterprises in Uzbekistan. Sustainability, 18(11), 5739. https://doi.org/10.3390/su18115739
Berman, R. (2018). Beyond the last touch: Attribution in online advertising. Marketing Science, 37(5), 771–792. https://doi.org/10.1287/mksc.2018.1104
Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2023). Digital Business Strategy: Toward a Next Generation of Insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37:2.3
Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing (7th ed.). Pearson.
Colot, C., Baecke, P. & Linden, I. (2021). Leveraging fine-grained mobile data for churn detection through Essence Random Forest. Journal of Big Data, 8, 63. https://doi.org/10.1186/s40537-021-00451-9
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
Ding, M., Dong, S., & Grewal, R. (2024). Generative AI and usage in marketing classroom. Customer Needs and Solutions, 11(1), 5. https://doi.org/10.1007/s40547-024-00145-2
Di Virgilio, F., Dimitrov, R., Dorokhova, L., Yermolenko, O., Dorokhov, O., & Petrova, M. (2023). Innovation factors for high and middle-income countries in the innovation management context. Access to science, business, innovation in the digital economy, ACCESS Press, 4(3), 434–452. https://doi.org/10.46656/access.2023.4.3(8)
Dwivedi, Y. K., Hughes, L., Ismagilova, E. et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Dyachenko, Yu., Nenkov, N., Petrova, M., Skarga-Bandurova, I., & Soloviov, O. (2018). Approaches to Cognitive Architecture of Autonomous Intelligent Agent. Biologically Inspired Cognitive Architectures, 26, 130–135. https://doi.org/10.1016/j.bica.2018.10.004
Ford, J., Jain, V., Wadhwani, K., & Gupta, D. (2023). AI advertising: An overview and guidelines, Journal of Business Research, 166. https://doi.org/10.1016/j.jbusres.2023.114124
Gattermann-Itschert, T., & Thonemann, U. (2022). Proactive customer retention management in a non-contractual B2B setting based on churn prediction with random forests. Industrial Marketing Management, 107, 134–147. https://doi.org/10.1016/j.indmarman.2022.09.023
Geldiev, E., Nenkov, N., & Petrova, M. (2018). Exercise of Machine Learning Using Some Python Tools and Techniques. CBU International conference proceedings 2018: Innovations in Science and Education, 21.-23.03.2018, 1062–1070. https://doi.org/10.12955/cbup.v6.1295
Genesis AI. (2026). AI-generated ads vs Human-made: CTR, CPA, and ROAS compared. https://getgenesis.app/blog/ai-generated-ads-performance
Grewal, D., Satornino, C. B., & Davenport, T. H. (2025). How generative AI is shaping the future of marketing. Journal of the Academy of Marketing Science, 53(3), 702–722. https://doi.org/10.1007/s11747-024-01064-3
Gulbasi, A. (2025). Artificial Intelligence in Marketing: Next Generation Strategies and Applications. Uluslararası Yonetim Akademisi Dergisi, 8(3), 786–801. https://doi.org/10.33712/mana.1624405
Hkiri, B., & Aloui, C. (2025). Correlating Investor Sentiments and Saudi Stock Market Behavior: A Wavelet-Based Approach. Access to science, business, innovation in the digital economy, ACCESS Press, 6(3), 599–614. https://doi.org/10.46656/access.2025.6.3(8)
Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 24(1), 3–16. https://doi.org/10.1177/1094670517752459
Huh, J., Nelson, M. R., & Russell, C. A. (2023). ChatGPT, AI advertising, and advertising research and education. Journal of Advertising, 52(4), 477–482. https://doi.org/10.1080/00913367.2023.2227013
Iskendirova, S., Amirova, A., Daueshova, A., Zhanseitov, A., Ismailova, R. (2025). Adapting to the AI revolution: comparative analysis of national workforce strategies. Access to science, business, innovation in digital economy, ACCESS Press, 6(3), 532–545. https://doi.org/10.46656/access.2025.6.3(4)
Jing, Y. (2025). Comparing Machine Learning Models for House Price Prediction: Linear Regression, Decision Tree, and Random Forest. ITM Web of Conferences. https://doi.org/10.1051/itmconf/20258001031
Klein, M. & Kutlar, A. (2024). A comparative experimental study on artificial intelligence- and human-driven social media marketing campaigns. Pressacademia. https://doi.org/10.17261/Pressacademia.2024.1935
Kumar, V., Ramachandran, D., & Kumar, B. (2021). Influence of new-age technologies on marketing: A research agenda. Journal of Business Research, 125, 864–877. https://doi.org/10.1016/j.jbusres.2020.01.007
Lamberton, C., & Stephen, A. T. (2016). A Thematic Exploration of Digital, Social Media, and Mobile Marketing Research's Evolution from 2000 to 2015 and an Agenda for Future Research. Journal of Marketing, 80(6), 146–172. https://doi.org/10.1509/jm.15.0415
Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966–2981. https://doi.org/10.2139/ssrn.2852260
Linde, I., & Petrova, M. (2018) The challenges of formalization and modelling of Higher Education Institutions in the 21st century. CBU International conference proceedings 2018: Innovations in Science and Education, 21.-23.03.2018, 303–308. https://doi.org/10.12955/cbup.v6.1173
Masnita, Y., Kasuma, J., Zahra, A., & Wilson, N. (2024). Artificial intelligence in marketing: Literature review and future research agenda. Journal of System and Management Sciences, 14(1), 120–140. https://doi.org/10.33168/JSMS.2024.0108
Matyushenko, I., Hlibko, S., Petrova, M. M., Pasmor, M. S., & Loktionova, M. (2020). Assessment of the development of foreign trade in high-tech production of Ukraine under the association with the EU. Business, Management and Education, 18(1), 157–182. https://doi.org/10.3846/bme.2020.11578
Mouammine, Y. (2026). AI in customer experience and digital marketing: A bibliometric and thematic review (2004–2025). Cogent Business & Management, 13(1). https://doi.org/10.1080/23311975.2026.2613596
Nikolova-Alexieva, V., Alexieva, I., Valeva, K., & Petrova, M. (2022). Model of the Factors Affecting the Eco-Innovation Activity of Bulgarian Industrial Enterprises. Risks, 10(9), 178. https://doi.org/10.3390/risks10090178
Petrova, M., Sushchenko, O., Vovk, K., Akhmedyarov, Y., & Pohuda, N. (2026). Comparative Analysis of the Features of Remarketing Implementation in the Context of Digital Transformation: Service vs. Manufacturing Sectors. Sustainability, 18(4), 1777. https://doi.org/10.3390/su18041777
Petrova, M., Sushchenko, O., Trunina, I., & Dekhtyar, N. (2018). Big Data Tools in Processing Information from Open Sources. IEEE First International Conference on System Analysis & Intelligent Computing (SAIC-2018)) Kyiv, Ukraine 08-12 October 2018. https://doi.org/10.1109/SAIC.2018.8516-800
Pukala, R. (2021). Impact of financial risk on the operation of start-ups. Access to science, business, innovation in digital economy, ACCESS Press, 2(1), 40–49. https://doi.org/10.46656/access.2021.2.1(4)
Taboola. (2023). New Study: AI Ads Match Human Creative in Major Report from Columbia University, Harvard University, Technical University of Munich, and Carnegie Mellon University; Taboola Data Shows AI Wins by Appearing Authentically Human and Prioritizing Visual Trust Signals. https://www.taboola.com/press-releases/genai-ads-study-2026
Tang, Z. (2024). The role of AI and ML in transforming marketing strategies: Insights from recent studies. Advances in Economics, Management and Political Sciences, 108. https://doi.org/10.54254/2754-1169/108/20242009
Wedel, M., & Kannan, P. K. (2022). Marketing analytics for data-rich environments. Journal of Marketing, 86(1), 97–121. https://doi.org/10.1509/jm.15.0413
Wu, Z., Zang, C., Wu, C., Deng, Z., Shao, X., & Liu, W. (2022). Improving Customer Value Index and Consumption Forecasts Using a Weighted RFM Model and Machine Learning Algorithms. Journal of Global Information Management (JGIM), 30(3), 1–23. https://doi.org/10.4018/JGIM.20220701.oa1
Ziakis, C., & Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: Insights from a comprehensive review. Information, 14(12), 664. https://doi.org/10.3390/info14120664
This work is licensed under a Creative Commons Attribution 4.0 International License.