A Hybrid Framework for Personalized Digital Marketing: Merging Machine Learning with HCI Principles

Abstract

Digital marketing increasingly relies on data science for audience targeting and performance analytics. However, these efforts often overlook the role of human-computer interaction (HCI) in shaping user experiences, leading to suboptimal engagement despite algorithmic personalization. Existing models emphasize prediction and targeting but rarely integrate HCI principles into the design and deployment of digital campaigns. This disconnect results in high bounce rates, low interaction quality and poor user retention. This paper bridges the gap by proposing an integrated framework that combines data-driven decision-making with user-centred design. The framework applies machine learning to behavioural data while embedding HCI heuristics to personalize and enhance digital interfaces. Experimental deployment on an e-commerce platform showed a 20.4% increase in user engagement and a 14.7% boost in conversion rates, validated through A/B testing over a four-week period with 10,000 users. These improvements were statistically significant (p < 0.05). The proposed framework can be adopted by digital marketers and UX teams to co-design campaigns that are both analytically optimized and experientially intuitive. The current study is limited to short-term interactions on a single platform. Future work will generalize the model across industries and explore long-term behavioural adaptation and brand loyalty impact.