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Think Global, Advertise Local: How AI Personalizes Ads for European Market

DOI: 10.4236/oalib.1113608, PP. 1-19

Subject Areas: Artificial Intelligence, Advertising

Keywords: Artificial Intelligence, Personalized Advertising, Cultural Sensitivity

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Abstract

The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed marketing strategies, offering unprecedented opportunities for personalization, particularly in the diverse landscape of the European market. This article investigates the theme “Think Global, Advertise Local”, focusing on how AI technologies empower businesses to craft personalized advertisements that are finely tuned to local cultural contexts and consumer preferences. By employing principles of glocalization, AI-driven tools allow marketers to analyze vast datasets, revealing insights into consumer behavior and cultural nuances that enhance customer engagement and drive brand loyalty. This study utilizes a mixed-methods approach that includes quantitative surveys and qualitative interviews to examine the effectiveness of AI in personalizing ads and the ethical considerations that arise from its implementation. Preliminary findings illustrate that AI’s capacity to tailor advertising content significantly influences consumer perceptions and engagement rates, ultimately impacting overall marketing effectiveness across various European markets. Furthermore, the study addresses potential ethical challenges, including data privacy concerns and algorithmic bias, emphasizing the necessity of responsible AI deployment in marketing practices. The insights generated from this research highlight the critical balance between technological innovation and cultural respect in advertising, suggesting that businesses must navigate this complex interplay to achieve sustained success in an increasingly digital marketplace.

Cite this paper

Asserrhine, H. and Zhu, P. (2025). Think Global, Advertise Local: How AI Personalizes Ads for European Market. Open Access Library Journal, 12, e3608. doi: http://dx.doi.org/10.4236/oalib.1113608.

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