AI-Augmented Marketing Mix Optimization: A Cloud-Native Machine Learning Architecture for Secure Digital Advertising Analytics on SAP HANA
DOI:
https://doi.org/10.15680/IJCTECE.2025.0805022Keywords:
Marketing mix optimization, Cloud-native architecture, Machine learning, AI-driven analytics, SAP HANA, Digital advertising, Secure data analytics, Multi-channel attribution, Predictive modeling, Marketing intelligenceAbstract
The digital advertising ecosystem is becoming increasingly complex, requiring intelligent, scalable, and secure analytical frameworks to optimize marketing investments. This study presents an AI-augmented, cloud-native machine learning architecture designed to enhance marketing mix optimization and enable advanced digital advertising analytics on SAP HANA. The proposed system integrates real-time data ingestion, predictive modeling, and automated optimization workflows within a secure cloud environment, ensuring high-performance processing and compliance with enterprise-level data governance standards. Machine learning models—including multi-channel attribution, forecasting algorithms, and reinforcement learning—are employed to quantify channel effectiveness, predict customer responses, and dynamically allocate marketing budgets. AI-driven augmentation layers further enhance insight generation by providing scenario simulations, performance benchmarking, and adaptive recommendation capabilities. SAP HANA’s in-memory computing accelerates analytical operations, supporting large-scale campaign telemetry, behavioral tracking, and cross-platform data fusion. The architecture significantly improves accuracy, reduces latency, and strengthens operational security for digital advertising analytics. Overall, this work delivers a robust, transparent, and enterprise-ready approach to marketing mix optimization, offering actionable intelligence for data-driven marketing strategies.
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