AI-DRIVEN OPTIMISATION OF DIGITAL MARKETING CAMPAIGNS: A PREDICTIVE FRAMEWORK FOR CUSTOMER RESPONSE AND CAMPAIGN ALLOCATION

Abstract

The rapid expansion of digital platforms has fundamentally changed the way organisations design, implement, and evaluate marketing campaigns. The increasing volume of customer interaction, behavioural, transactional, and campaign-level data creates significant opportunities for artificial intelligence (AI)-based predictive decision-making. However, conventional digital marketing practices frequently remain focused on retrospective performance measurement rather than anticipating customer responses and dynamically allocating marketing resources. This paper proposes an AI-driven predictive framework that integrates customer response prediction, audience segmentation, personalisation, attribution analysis, and dynamic campaign allocation within a unified decision-making architecture. The proposed framework uses historical customer and campaign data to estimate the probability of customer response and subsequently allocates marketing resources according to expected campaign value. The framework is conceptualised as a continuous feedback cycle consisting of data collection, prediction, attribution, allocation, response measurement, and model learning. Such an approach can support a transition from static campaign management towards adaptive and evidence-based marketing optimisation. At the same time, the paper emphasises the importance of data quality, privacy, transparency, algorithmic fairness, and human oversight in AI-enabled marketing. The proposed framework provides a conceptual foundation for future empirical research and practical implementation of predictive marketing optimisation.