Author(s)

Rina Sato

Affiliation(s)

School of Business, Kyoto University, Japan.

Corresponding Author

Rina Sato

ABSTRACT

As customer behaviors evolve rapidly in the digital economy, traditional static marketing strategies struggle to maintain effectiveness. Adaptive marketing interventions, empowered by artificial intelligence (AI), offer a dynamic solution to personalize customer engagement, optimize campaign timing, and maximize return on investment (ROI). This study proposes a comprehensive framework for designing AI-driven adaptive marketing interventions in multi-channel environments. By leveraging machine learning (ML) algorithms such as reinforcement learning (RL), neural networks, and customer clustering, we demonstrate how marketers can dynamically adjust messaging, discounts, and product recommendations in response to real-time behavioral cues. Experimental simulations on synthetic datasets show significant improvements in conversion rates, customer lifetime value (CLV), and campaign efficiency compared to baseline static strategies. The findings provide empirical support for integrating AI into marketing decision-making processes and offer practical insights into implementation challenges and scalability considerations.

KEYWORDS

Adaptive marketing; Artificial intelligence; Reinforcement learning; Customer segmentation; Campaign optimization; Personalized marketing; Marketing automation

CITE THIS PAPER

Rina Sato. Designing adaptive marketing interventions using artificial intelligence. Social Science and Management. 2025, 2(2): 84-89. DOI: https://doi.org/10.61784/ssm3053.

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