An AI and Machine Learning–Based Secure Cloud Framework for Marketing Mix Modeling Across Healthcare and Financial Domains
DOI:
https://doi.org/10.15662/IJRAI.2025.0805011Keywords:
AI-powered cloud framework, machine learning, marketing mix modeling, deep learning, secure cloud computing, healthcare analytics, financial analytics, data security, privacy preservation, big data analytics, predictive modeling, cloud-based marketing analyticsAbstract
The convergence of healthcare and financial data in cloud environments presents significant opportunities for advanced analytics, while simultaneously introducing challenges related to data security, privacy, and modeling complexity. This paper proposes an AI and machine learning–based secure cloud framework designed to support Marketing Mix Modeling (MMM) across healthcare and financial domains. The framework leverages deep learning and statistical machine learning techniques to capture nonlinear relationships among marketing channels, customer behavior, and domain-specific outcomes, enabling accurate attribution and optimized budget allocation.A secure, cloud-native architecture is introduced, incorporating encrypted data pipelines, role-based access control, and privacy-aware data integration mechanisms to ensure regulatory compliance and trust. Machine learning–driven MMM models are trained on heterogeneous, large-scale datasets using scalable cloud infrastructure, while automated feature engineering and model selection improve robustness and predictive performance. The proposed framework supports cross-domain analytics by harmonizing healthcare and financial data schemas and enabling transferable insights across industries. Experimental results demonstrate enhanced forecasting accuracy, improved marketing effectiveness measurement, and strong scalability compared to traditional MMM approaches. The framework provides a secure and intelligent foundation for data-driven marketing decision-making in cloud-based healthcare and financial ecosystems.
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