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International Journal of Science, Strategic Management and Technology

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PERSONALIZED MARKETING THROUGH ARTIFICIAL INTELLIGENCE: A STUDY

AUTHORS:
Dr. Shilpi Bhalla
Mentor
Affiliation
DOI: 10.55041/ijsmt.v2i4.119
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

The acceleration of digital transformation in India has fundamentally reshaped contemporary marketing paradigms, positioning Artificial Intelligence (AI) as a strategic enabler of hyper-personalized customer engagement. Unlike conventional segmentation-based approaches, AI-driven personalized marketing leverages advanced machine learning models, predictive analytics, and behavioral data mining to generate granular consumer insights and real-time decision support. Indian digital enterprises such as Myntra, Flipkart, and Zomato deploy sophisticated recommendation engines and dynamic targeting systems that analyze multidimensional datasets—including transaction history, clickstream behavior, contextual location data, and temporal consumption patterns—to deliver individualized product suggestions, customized promotional interventions, and adaptive user interfaces (Kshetri, 2021; Rana et al., 2022

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 04
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Apr 10 2026
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Bhalla, S. (2026). Personalized Marketing Through Artificial Intelligence: A Study. International Journal of Science, Strategic Management and Technology, 02(04). https://doi.org/10.55041/ijsmt.v2i4.119

Bhalla, Shilpi. "Personalized Marketing Through Artificial Intelligence: A Study." International Journal of Science, Strategic Management and Technology, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i4.119.

Bhalla, Shilpi. "Personalized Marketing Through Artificial Intelligence: A Study." International Journal of Science, Strategic Management and Technology 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i4.119.

References
1.Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing: Strategy, implementation and practice (7th ed.). Pearson.

2.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

3.Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9

4.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

5.Martin, K., & Murphy, P. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155. https://doi.org/10.1007/s11747-016-0495-4

6.Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.

7.Bhatia, M., & Bhatia, S. (2023). Artificial intelligence in Indian digital marketing: Opportunities and ethical considerations. International Journal of Information Management Data Insights, 3(1), 100–112.

8.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, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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