The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Application of Machine Learning Algorithms in Personalized Marketing
Abstract
The win-win relationship between artificial intelligence and the cutting-edge field of targeted advertising. Companies nowadays are employing machine learning algorithms to develop targeted marketing strategies because they simply cannot afford to ignore their clients. This article delves into significant machine learning approaches such collaborative filtering, content-based filtering, clustering, and predictive modelling to illuminate their roles in offering personalised experiences. Examining real-world applications across many industries, such as e-commerce, social media, email marketing, and retail, highlights successful implementations. Although the piece focuses on the positives, such increased conversion rates and satisfied consumers, it also examines the negatives, like concerns about data privacy and algorithmic biases. The discusses the ethical concerns brought up by highly personalised advertising and foresees future advances such as natural language processing and reinforcement learning. Lastly, this article provides excellent research on the ways in which machine learning algorithms have changed personalised marketing, which can help businesses adapt to the dynamic nature of customer engagement.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Audiences were segmented only coarsely (by broad demographics or media channels), and campaigns unfolded on fixed schedules (Saheb et al., 2022;Sedkaoui & Benaichouba, 2024;Thirumagal et al., 2024;Yadav, S. J., 2024).”
From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Thirumagal et al., 2024
In the citing paper
“Mass marketing, which dominated the last century, operated on broadcast principles: a single message (a TV commercial, print ad, or billboard) was disseminated to millions, with minimal personalization.”
From Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence (2025) · cites this paper as Thirumagal et al., 2024
Citations
Cites
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Research works printed but not linked: 10
- Doe, 2018, Collaborative Filtering for Personalized Content Delivery, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1200
- Smith, Predictive Modeling in Personalized Marketing Campaigns, 300
- Johnson, 2020, Machine Learning for Marketing: Algorithms and Applications
- Rodriguez, 2017, Clustering Algorithms for Customer Segmentation: A Comparative Research, IEEE Transactions on Knowledge and Data Engineering, 28, 800
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- Martin, Social Media Marketing: Leveraging Machine Learning for Content Delivery, 400
- 10.1093/bioinformatics/btad755 · DOI
- Walker, 2016, Machine Learning Approaches to Natural Language Processing in Personalized Marketing, IEEE Transactions on Neural Networks and Learning Systems, 25, 1600
- Turner, Impact of Machine Learning on Retail: A Case Research on Customer Segmentation, 100
Cited by
Sorted by citations
Artificial Intelligence in Marketing Management: Enhancing Customer Engagement and Personalization
Verma, C., Vijayalakshmi, P. S., Chaturvedi, N., Umesh, U., Rai, A., & Ahmad, A. Y. A. B. (2025) · International Conference on Pervasive Computational Technologies (ICPCT) · doi:10.1109/icpct64145.2025.10940626 · Cited by 27
Kaponis, A., Μaragoudakis, M., & Sofianos, K. C. (2025) · Computers · doi:10.3390/computers14060211 · Cited by 31
Ethical Challenges and Bias in AI-Driven Marketing
Alam, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4327-3.ch003 · Cited by 13
Surveillance Capitalism, Consumer Subjectivity and Marketing in the Age of Artificial Intelligence
Marchand, É., Durand, T., & Dubois, M. (2025) · Social Science Chronicle · doi:10.56106/ssc.2025.001 · Cited by 5
Evaluating the Role of Social Media Advertisements on Consumer Engagement Using Machine Learning
Nagar, P., & Purohit, S. (2025) · Global Conference in Emerging Technology (GINOTECH) · doi:10.1109/ginotech63460.2025.11076844 · Cited by 1
Metadata
- Authors
- Thirumagal, P. G., Kishore Bhattacharjee, D., Dorbala, R., Palav, M. R., S, V., & Mahajan, V.
- Affiliations
- Vels University (IN)
- Amity University,Patna,Bihar
- Lovely Professional University (IN)
- Global Business School & Research Centre,MBA Department,Pune,Maharashtra,India
- Kalasalingam Academy of Research and Education,Department of Commerce,Srivilliputtur,Tamil Nadu
- Symbiosis Centre for Management & Human Resource Development (SCMHRD),Department of Marketing,Pune,Maharashtra
- Venue
- 2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST)
- Publisher
- IEEE
- Published
- 2024-04-09
- Pages
- 165-170
- OpenAlex
- W4400315492
- Cited by
- 7
- References
- 6
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1
- FWCI
- 9.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Recent Trends in Computer Science and Technology (ICRTCST) (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- personalized marketing, targeted advertising, collaborative filtering, content-based filtering, clustering, predictive modeling, e-commerce, social media advertising, email marketing, retail trade, data privacy, algorithmic bias, ethical considerations, natural language processing, reinforcement learning
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- The paper
- Thirumagal, P. G., Kishore Bhattacharjee, D., Dorbala, R., Palav, M. R., S, V., & Mahajan, V. (2024). Application of Machine Learning Algorithms in Personalized Marketing. 2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST). https://doi.org/10.1109/icrtcst61793.2024.10578463
