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AI in Consumer Behavior Analysis and Digital Marketing: A Strategic Approach

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  • Nane Davtyan

Abstract

The rapid advancement of Artificial Intelligence (AI) has revolutionized consumer behavior analysis and digital marketing strategies by enabling personalized and efficient data-driven approaches. AI-driven tools like predictive analytics, natural language processing (NLP), machine learning, and programmatic advertising allow marketers to process vast amounts of real-time consumer data, facilitating optimized campaign performance and precise targeting. This paper explores the integration of AI in marketing, highlighting its role in enhancing predictive analytics, sentiment analysis, and real-time segmentation. Compared to traditional methods, AI-driven insights significantly improve engagement, accuracy, and return on investment (ROI). AI also plays a vital role in marketing automation, allowing dynamic adjustments in campaigns, ad placements, and content creation, improving efficiency and reducing costs. However, AI’s reliance on consumer data raises concerns regarding data privacy and algorithmic bias, especially in targeting. This paper stresses the importance of ensuring transparency, fairness, and regular audits in AI systems to maintain consumer trust and promote ethical AI use. Future research directions are discussed, focusing on enhancing transparency and algorithmic accountability while navigating the ethical challenges of AI in marketing.

Suggested Citation

  • Nane Davtyan, 2024. "AI in Consumer Behavior Analysis and Digital Marketing: A Strategic Approach," SBS Swiss Business School Research Conference (SBS-RC) 005, SBS Swiss Business School.
    • Handle: RePEc:bfv:sbsrec:005
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    File URL: https://research.sbs.edu/sbsrc/SBSRC24_Paper05.pdf
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    References listed on IDEAS

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    1. Surajit Bag & Gautam Srivastava & Md Mamoon Al Bashir & Sushma Kumari & Mihalis Giannakis & Abdul Hannan Chowdhury, 2021. "Journey of customers in this digital era: Understanding the role of artificial intelligence technologies in user engagement and conversion," Post-Print hal-03852328, HAL.
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