AI-Driven Consumer Behavior Prediction and Brand Relationship Marketing: Technological Applications and Theoretical Mechanisms

Main Article Content

Dan Li
College of Foreign Languages and Cultures (CFLC), Institute of XIA MEN University, Xiamen, China

DOI: https://doi.org/10.70267/icbms.2601.289295

Keywords

artificial intelligence, consumer behavior prediction, customer engagement, brand relationship marketing, trust

Abstract

Artificial intelligence (AI) has increasingly been applied in marketing to predict consumer behavior and enhance personalized interactions. While existing research emphasizes technological efficiency and predictive accuracy, limited studies systematically examine how AI-driven consumer behavior prediction influences long-term brand relationship marketing. This study provides a narrative literature review to analyze the theoretical mechanisms connecting AI capability, customer engagement, and brand relationship outcomes. Drawing upon customer journey theory, customer engagement theory, and customer equity theory, this paper proposes that engagement serves as a mediating mechanism between AI-driven personalization and brand performance. Furthermore, trust and data privacy concerns are identified as moderating factors shaping the effectiveness of AI-enabled marketing strategies. By integrating technological and relational perspectives, this study clarifies the theoretical foundation of AI in relationship marketing and identifies future research directions for empirical validation.

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References

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Published
May 26, 2026
Conference Proceedings Volume
Vol. 13 (2026): Proceedings of the 3rd International Conference on Business, Management and Sustainability (ICBMS 2026)
Section
Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Li, Dan. “AI-Driven Consumer Behavior Prediction and Brand Relationship Marketing: Technological Applications and Theoretical Mechanisms”. Exploring Science Academic Conference Series, vol. 13, May 2026, pp. 289-95, https://doi.org/10.70267/icbms.2601.289295.