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Cited by 8Digital Marketing and Social MediaRecommender Systems and TechniquesAI in Service Interactions

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

Thirumagal, P G, Kishore Bhattacharjee, Dr., Dorbala, Rajesh, Palav, Manesh R., S, Viji, Mahajan, Vaishali·2024·2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST)
DOI 10.1109/icrtcst61793.2024.10578463
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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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Crossref · 10.1109/icrtcst61793.2024.10578463

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Citations

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Show all 6 references

Research works printed but not linked: 10

  1. Doe, 2018, Collaborative Filtering for Personalized Content Delivery, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1200
  2. Smith, Predictive Modeling in Personalized Marketing Campaigns, 300
  3. Johnson, 2020, Machine Learning for Marketing: Algorithms and Applications
  4. Rodriguez, 2017, Clustering Algorithms for Customer Segmentation: A Comparative Research, IEEE Transactions on Knowledge and Data Engineering, 28, 800
  5. White, 2022, Dynamic Pricing Strategies for Personalized Email Marketing, IEEE Journal of Selected Topics in E-commerce, 12, 500
  6. Carter, 2019, Ethical Considerations in the Implementation of Personalized Marketing, IEEE Transactions on Technology and Society, 8, 50
  7. Martin, Social Media Marketing: Leveraging Machine Learning for Content Delivery, 400
  8. 10.1093/bioinformatics/btad755 · DOI
  9. Walker, 2016, Machine Learning Approaches to Natural Language Processing in Personalized Marketing, IEEE Transactions on Neural Networks and Learning Systems, 25, 1600
  10. Turner, Impact of Machine Learning on Retail: A Case Research on Customer Segmentation, 100

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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
ORCID
Venue
2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST)
Publisher
IEEE
Published
2024-04-09
Pages
165-170
DOI
10.1109/icrtcst61793.2024.10578463
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
Licence
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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