DESIGNING ADAPTIVE MARKETING INTERVENTIONS USING ARTIFICIAL INTELLIGENCE

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.