A Cybersecure AI–ML Analytics Platform for Marketing Mix Modeling Enhancing Digital Advertising Insights in Cloud Environments

Abstract

In the era of digital advertising, marketers face mounting challenges in integrating cross-channel data, optimizing media spend, and safeguarding customer data. This paper proposes a conceptual design and proof-of-concept implementation of a cybersecure AI–ML analytics platform tailored for Marketing Mix Modeling (MMM) in cloud environments. The platform leverages advanced machine learning (ML) methods to deliver more accurate, granular and dynamic attributions of marketing spend on business outcomes (sales, conversions, ROI), while embedding robust security, privacy, and compliance protections to safeguard sensitive customer and marketing data. Incorporating techniques such as Bayesian hierarchical modeling, time-varying coefficients, and non-linear adstock and saturation functions, the platform enhances predictive power and interpretability beyond traditional linear econometric MMM. Concurrently, the architecture employs cloud-native security best practices — including data isolation, encryption at rest and in transit, fine-grained access control, and isolation via confidential computing or “data clean room” frameworks — to ensure compliance and protect data integrity. Testing on synthetic and anonymized real-world datasets shows that the platform can attribute channel-level contributions with higher accuracy (reduced error variance), better capture carryover and diminishing return effects, and provide actionable budget-reallocation scenarios, while preserving data privacy and minimizing security risks. The results suggest that a cybersecure AI–ML MMM platform can bridge the gap between marketing effectiveness analysis and regulatory/operational requirements, offering a viable path forward for privacy-conscious, data-driven marketing organizations.