Targeted and Personalized Online Advertising in the Age of Artificial Intelligence (AI): A Literature Review and Research Agenda

Document Type : Review Paper

Authors

1 Mahan Business School (Marketing department)

2 Department of Industrial Engineering Alzahra University

Abstract

This study aims to provide a comprehensive evaluation of current machine learning (ML) algorithms employed in targeted and personalized advertising. It reveals key findings and conclusions from a wide range of sources, offering readers a concise summary. The study addresses the gap by identifying and analyzing the most significant machine learning-based targeting methods utilized in the recent studies. This helps readers understand the strengths and weaknesses of different approaches and keeps them up-to-date with the most recent advancements and best practices. Employing the PRISMA methodology, the review systematically examines existing literature on ML-driven targeted advertising. It identifies effective ML methods and strategies, presenting real-world examples to illustrate their practical implementation. Reviewing key findings from existing literature, the analysis identifies the most effective ML methods for targeted advertising. It also examines three research questions across three key dimensions: targeting, personalizing, and predicting customer preferences. This study proposes a novel theoretical framework that elucidates the application of ML in targeted advertising. Specifically, the study explores ML algorithms that enhance precision in each dimension. Key models include Long Short-Term Memory (LSTM) networks for analyzing historical customer data, Convolutional Neural Networks (CNN) for image recognition tasks, and Factorization Machines for capturing feature interactions in click-through rate (CTR) predictions. Additionally, traditional models such as logistic regression, decision trees, random forests, and support vector machines (SVM) are utilized for classification tasks, while unsupervised learning techniques like k-means clustering and hierarchical clustering facilitate user segmentation based on behavioral and demographic similarities. These models collectively enable marketers to derive actionable insights, optimize advertising content, and improve overall campaign performance. By consolidating key findings from existing literature on ML-driven targeted advertising, this study offers a valuable resource for understanding current trends and gaps. It also proposes future research directions, highlighting potential areas for further exploration, which can inspire new studies and innovations in the field.

Keywords


Akter, S., Dwivedi, Y. K., Sajib, S., Biswas, K., Bandara, R. J., & Michael, K. (2022). Algorithmic bias in machine learning-based marketing models. Journal of Business Research, 144, 201–216. https://doi.org/10.1016/j.jbusres.2022.01.083
Amalraj Victoire, T., Karunamurthy, A., Subitsha, B., & Kevin, A. E. (2023). Leveraging Artificial Intelligence in Marketing and Advertising: Unleashing the Power of Advanced Technologies. International Research Journal of Engineering and Technology. www.irjet.net
Arasu, B. S., Seelan, B. J. B., & Thamaraiselvan, N. (2020). A machine learning-based approach to enhancing social media marketing. Computers and Electrical Engineering, 86. https://doi.org/10.1016/j.compeleceng.2020.106723
Argan, M., Dinc, H., Kaya, S., & Argan, M. T. (2022). Artificial Intelligence (AI) in Advertising: Understanding and Schematizing the Behaviors of Social Media Users. Advances in Distributed Computing and Artificial Intelligence Journal, 11(3), 331–348. https://doi.org/10.14201/adcaij.28331
Bhatt, C., Bangwal, D., Purohit, U. N., Chauhan, R., B P, A. P., & Singh, T. (2023). Behavior Analysis Using User’s Search History Through Machine Learning. 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), 587–591. https://doi.org/10.1109/ICCSAI59793.2023.10420857
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. In International Journal of Information Management (Vol. 57). Elsevier Ltd. https://doi.org/10.1016/j.ijinfomgt.2020.102225
Boyko, N., & Kholodetska, Y. (2022). Using Artificial Intelligence Algorithms in Advertising. International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2022-November, 317–321. https://doi.org/10.1109/CSIT56902.2022.10000819
Chen, Y. (2023). Comparing content marketing strategies of digital brands using machine learning. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-01544-x
Choi, J. A., & Lim, K. (2020). Identifying machine learning techniques for classification of target advertising. In ICT Express (Vol. 6, Issue 3, pp. 175–180). Korean Institute of Communications Information Sciences. https://doi.org/10.1016/j.icte.2020.04.012
Ciuchita, R., Gummerus, J. K., Holmlund, M., & Linhart, E. L. (2023). Programmatic advertising in online retailing: consumer perceptions and future avenues. Journal of Service Management, 34(2), 231–255. https://doi.org/10.1108/JOSM-06-2021-0238
De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & von Wangenheim, F. (2020). Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing, 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007
De Mauro, A., Sestino, A., & Bacconi, A. (2022). Machine learning and artificial intelligence use in marketing: a general taxonomy. Italian Journal of Marketing, 2022(4), 439–457. https://doi.org/10.1007/s43039-022-00057-w
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
Ebrahimi, P., Basirat, M., Yousefi, A., Nekmahmud, M., Gholampour, A., & Fekete‐farkas, M. (2022). Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches. Big Data and Cognitive Computing, 6(2). https://doi.org/10.3390/bdcc6020035
Ford, J., Jain, V., Wadhwani, K., & Gupta, D. G. (2023). AI advertising: An overview and guidelines. Journal of Business Research, 166. https://doi.org/10.1016/j.jbusres.2023.114124
Gao, B., Wang, Y., Xie, H., Hu, Y., & Hu, Y. (2023). Artificial Intelligence in Advertising: Advancements, Challenges, and Ethical Considerations in Targeting, Personalization, Content Creation, and Ad Optimization. In SAGE Open (Vol. 13, Issue 4). SAGE Publications Inc. https://doi.org/10.1177/21582440231210759
Geru, M. & M. A.-E. & C. A. & M. A. (2018). Using Artificial Intelligence on Social Media’s User Generated Content for Disruptive Marketing Strategies in eCommerce Développement durable, responsabilité sociale, culture et performance d’entreprise View project The Marketing Simulations Online Knowledge Center (MSOKC) View project Using Artificial Intelligence on Social Media’s User Generated Content for Disruptive Marketing Strategies in eCommerce. Annals of the University Dunarea de Jos of Galati, 5–11. https://doi.org/10.26397/eai1584040911
Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.
Gupta, M., Kumar, R., Sharma, A., & Pai, A. S. (2023). Impact of AI on social marketing and its usage in social media: A review analysis. 2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023. https://doi.org/10.1109/ICCCNT56998.2023.10308092
Haleem, A., Javaid, M., Asim Qadri, M., Pratap Singh, R., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. In International Journal of Intelligent Networks (Vol. 3, pp. 119–132). KeAi Communications Co. https://doi.org/10.1016/j.ijin.2022.08.005
Hocutt, D. L. (2024). Composing with generative AI on digital advertising platforms. Computers and Composition, 71. https://doi.org/10.1016/j.compcom.2024.102829
Huai, J. (2021). Explore the digital transformation path of the advertising industry in the era of artificial intelligence. Proceedings - 2021 International Conference on Computer Information Science and Artificial Intelligence, CISAI 2021, 832–836. https://doi.org/10.1109/CISAI54367.2021.00168
Huang, M. H., & Rust, R. T. (2022). A Framework for Collaborative Artificial Intelligence in Marketing. Journal of Retailing, 98(2), 209–223. https://doi.org/10.1016/j.jretai.2021.03.001
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science , 30–50. https://doi.org/10.1007/s11747-020-00749-9/Published
Ijaz, M., Haq, U., David, T., Muhammad, A., & Haq, I. U. (2021). Role of Machine learning in Online Advertising. https://www.researchgate.net/publication/356909485
Jain, P., & Aggarwal, K. (2020). Transforming Marketing with Artificial Intelligence. International Research Journal of Engineering and Technology (IRJET), 7(7). https://doi.org/10.13140/RG.2.2.25848.67844
Joni Salminen, Bernard J. Jansen, & https://www.tandfonline.com/author/Mustak%2C+Mekhail. (2022). How Feature Changes of a Dominant Ad Platform Shape Advertisers’ Human Agency. International Journal of Electronic Commerce, 27, 1–33.
Kamal, M., & Bablu, T. A. (2022). Machine Learning Models for Predicting Click-Through Rates on social media: Factors and Performance Analysis International Journal of Applied Machine Learning and Computational Intelligence Machine Learning Models for Predicting Click-through Rates on social media: Factors and Performance Analysis. https://doi.org/10.1003/2023935959
Kshetri, N., Dwivedi, Y. K., Davenport, T. H., & Panteli, N. (2023). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. In International Journal of Information Management. Elsevier Ltd. https://doi.org/10.1016/j.ijinfomgt.2023.102716
Kuang, A. (2022). Construction of Personalized Advertising Accuracy Model Based on Artificial Intelligence. Proceedings - 2022 International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022, 395–398. https://doi.org/10.1109/AIARS57204.2022.00095
Lee, G. H., Lee, K. J., Jeong, B., & Kim, T. K. (2024). Developing Personalized Marketing Service Using Generative AI. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3361946
Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481–504. https://doi.org/10.1016/j.ijresmar.2020.04.005
Maddodi, C. B., & Upadhyaya, P. (2023). In-app advertising: a systematic literature review and implications for future research. In Spanish Journal of Marketing - ESIC. Emerald Publishing. https://doi.org/10.1108/SJME-05-2022-0120
Makalesi, A., Aydın, S., Gökhan Nalbant, K., Kelimeler, A., & Zeka Dijital Pazarlama, Y. (n.d.). The Significance of Artificial Intelligence in the Realms of Marketing, Advertising, and Branding inside the Metaverse Metaverse’de Pazarlama, Reklam ve Markalama Alanlarında Yapay Zekanın Önemi A R T I C L E I N F O. JOURNAL OF EMERGING ECONOMIES AND POLICY 2023, 8(2), 301–316.
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. International Journal of Surgery, 8(5), 336–341. https://doi.org/10.1016/j.ijsu.2010.02.007
Ngai, E. W. T., & Wu, Y. (2022). Machine learning in marketing: A literature review, conceptual framework, and research agenda. Journal of Business Research, 145, 35–48. https://doi.org/10.1016/j.jbusres.2022.02.049
Nour Sadeq, G. N. , J. Y. (2023). Impact of Artificial Intelligence on E-marketing. International Journal of Trend in Scientific Research and Development (IJTSRD). https://www.researchgate.net/publication/368916517
Plangger, K., Grewal, D., de Ruyter, K., & Tucker, C. (2022). The future of digital technologies in marketing: A conceptual framework and an overview. In Journal of the Academy of Marketing Science (Vol. 50, Issue 6, pp. 1125–1134). Springer. https://doi.org/10.1007/s11747-022-00906-2
Raphael, J., Madhusudana Rao, N., Bindu, A., & Gao, X. Z. (2022). Clustering-based Factorization Machines for Advertisement Click prediction. Procedia Computer Science, 215, 546–555. https://doi.org/10.1016/j.procs.2022.12.057
Ratchford, B. T. (2020). The history of academic research in marketing and its implications for the future. In Spanish Journal of Marketing - ESIC (Vol. 24, Issue 1, pp. 3–36). Emerald Group Holdings Ltd. https://doi.org/10.1108/SJME-11-2019-0096
Sánchez-Fernández, P., Baca Ruiz, L. G., & Pegalajar Jiménez, M. del C. (2023). Application of classical and advanced machine learning models to predict personality on social media. Expert Systems with Applications, 216. https://doi.org/10.1016/j.eswa.2022.119498
Shi, B., & Wang, H. (2023). An AI-enabled approach for improving advertising identification and promotion in social networks. Technological Forecasting and Social Change, 188. https://doi.org/10.1016/j.techfore.2022.122269
Xiong, W., Xiong, Z., & Tian, T. (2022). Who to show the ad to? Behavioral targeting in Internet advertising. Journal of Internet and Digital Economics, 2(1), 15–26. https://doi.org/10.1108/jide-12-2021-0023
Yang, Y., & Zhai, P. (2022). Click-through rate prediction in online advertising: A literature review. Information Processing and Management, 59(2). https://doi.org/10.1016/j.ipm.2021.102853
Yolanda Masnita, J. K. A. A. Z. N. W. W. M. (2023). Artificial Intelligence in Marketing: Literature Review and Future Research Agenda. Journal of System and Management Sciences, 14(1). https://doi.org/10.33168/JSMS.2024.0108
Zhao, H., Lyu, F., & Luo, Y. (2022). Research on the Effect of Online Marketing Based on Multimodel Fusion and Artificial Intelligence in the Context of Big Data. Security and Communication Networks, 2022. https://doi.org/10.1155/2022/1516543
Ziakis, C., & Vlachopoulou, M. (2023). Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information (Switzerland), 14(12). https://doi.org/10.3390/info14120664