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Account and Financial Management Journal
Article . 2025 . Peer-reviewed
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ZENODO
Article . 2025
License: CC BY
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ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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AI-Driven Marketing Personalization

Authors: Dr Janemary Thirusanku; Kamlesh Rao Thillai Raman;

AI-Driven Marketing Personalization

Abstract

The essence of the paper is a reflection on the necessity to integrate Artificial Intelligence (AI) in marketing with regards to personalization strategies. It is based on the modern literature from the year 2020-2025 and examines the potential of using AI to increase personalization and achieve the following results in marketing: enhanced consumer connection, organizational performance, and focus with stakeholder and sustainability objectives. The studies rely on some of the most significant theoretical constructs which include the Marketing Mix, the Stakeholder Theory, the Triple bottom-line and the Hunt Vitell Theory of Marketing Ethics to examine the ethical and positive impacts of AI personalization gadgets. Real-life case studies of companies, such as Netflix, H&M, Amazon, or Meta, were utilized in the discussion as an opportunity to identify the opportunities of AI, and the significant threats of AI regarding data privacy, biased algorithms, or manipulation of the consumer. The report presents recommendations, which are, and which require the emphasis on fairness, transparency, and ethical innovation. These include the implementation of effective AI, condolence logs on fairness, and advancing commercial analytics experiential attempts. The research indicates that organizations must strike a balance between the current rise in the technological world and irresponsible, humanistic approaches to marketing.

Keywords

Artificial Intelligence, marketing personalization, stakeholder theory, algorithmic bias, ethical marketing, transparency, triple bottom line, customer engagement, Artificial Intelligence, marketing personalization, stakeholder theory, algorithmic bias, ethical marketing, transparency, triple bottom line, customer engagement

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
gold