Secure AI-Based Marketing Mix Modeling: Cloud-Optimized Machine Learning for Advertising Effectiveness and Digital Media Analytics
DOI:
https://doi.org/10.15662/IJRAI.2025.0806020Keywords:
marketing mix modeling, machine learning, cloud computing, advertising effectiveness, digital media analytics, adstock, budget optimization, media mix, non-linear modeling, predictive analyticsAbstract
In the modern digital economy, marketers face increasing complexity in allocating advertising spend across multiple channels — online search, social media, display, and traditional media — while striving for measurable advertising effectiveness and return-on-investment (ROI). Traditional marketing mix modeling (MMM) approaches, often econometric or linear regression-based, struggle to accommodate non-linear spend-response relationships, carry-over (adstock) and saturation effects, or rapid shifts in channel performance. This study proposes a secure, cloud-optimized AI-based marketing mix modeling framework, leveraging scalable cloud infrastructure and machine learning (ML) to deliver dynamic, high-resolution insights into ad effectiveness and to optimize media spend across channels. The framework integrates data ingestion pipelines for multi-channel spend and performance data, applies ML models (e.g., gradient boosting, tree-based models) to estimate channel contributions, carry-over, interactions and saturation curves, and runs budget-optimization simulations to recommend spend allocation. We implement the framework using a simulated multi-channel dataset and conduct cross-validated experiments comparing ML-based MMM with conventional log-linear and interaction-based models. Results show that ML-based MMM improves predictive accuracy (measured by out-of-sample RMSE) by ~ 18 % over traditional models, uncovers non-linear saturation effects and interaction synergies, and yields optimized budgets that increase predicted ROI by 8–12%. The cloud-based design ensures data security, scalability, and near real-time model retraining and recommendation generation, enabling agile reallocation of ad spend in response to changing market conditions. The paper discusses the practical advantages — granularity, agility, better modeling fidelity — as well as challenges: data requirements, interpretability, and organizational complexity. This work demonstrates the viability of AI-powered, cloud-native MMM as a next-generation tool for advertising effectiveness and media analytics, and provides guidance for firms seeking to adopt evidence-based, data-driven media optimization.
References
1. Hanssens, D. M., Parsons, L. J., & Schultz, R. L. (2001). Market Response Models: Econometric and Time Series Analysis. Kluwer Academic / Springer.
2. Ramakrishna, S. (2022). AI-augmented cloud performance metrics with integrated caching and transaction analytics for superior project monitoring and quality assurance. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(6), 5647–5655. https://doi.org/10.15662/IJEETR.2022.0406005
3. Kumar, R. K. (2023). Cloud-integrated AI framework for transaction-aware decision optimization in agile healthcare project management. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(1), 6347–6355. https://doi.org/10.15680/IJCTECE.2023.0601004
4. Sivaraju, P. S. (2022). Enterprise-Scale Data Center Migration and Consolidation: Private Bank's Strategic Transition to HP Infrastructure. International Journal of Computer Technology and Electronics Communication, 5(6), 6123-6134.
5. Musunuru, M. V., Vijayaboopathy, V., & Selvaraj, A. (2023). Edge-Level Cookie Consent Enforcement using Bloom Filters in CDN Proxies. American Journal of Cognitive Computing and AI Systems, 7, 90-123.
6. Pandey, S., Gupta, S., & Chhajed, S. (2021). Marketing Mix Modeling (MMM) – Concepts and Model Interpretation. International Journal of Engineering Research & Technology, 10(06). IJERT
7. Jin, Y., Wang, Y., Sun, Y., Chan, D., & Koehler, J. (2017). Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects. Google Research. Google Research
8. Hanssens, D. M., Pauwels, K., Srinivasan, S., Vanhuele, M., & Yildirim, G. (2014). Consumer attitude metrics for guiding marketing mix decisions. Marketing Science, 33(4), 534–550.
9. Bhattacharya, P. (2008). Optimizing the marketing mix. Proceedings of the NorthEast SAS Users Group 21st Annual Conference.
10. Md Manarat Uddin, M., Rahanuma, T., & Sakhawat Hussain, T. (2025). Privacy-Aware Analytics for Managing Patient Data in SMB Healthcare Projects. International Journal of Informatics and Data Science Research, 2(10), 27-57.
11. Islam, M. S., Shokran, M., & Ferdousi, J. (2024). AI-Powered Business Analytics in Marketing: Unlock Consumer Insights for Competitive Growth in the US Market. Journal of Computer Science and Technology Studies, 6(1), 293-313.
12. Akhtaruzzaman, K., Md Abul Kalam, A., Mohammad Kabir, H., & KM, Z. (2024). Driving US Business Growth with AI-Driven Intelligent Automation: Building Decision-Making Infrastructure to Improve Productivity and Reduce Inefficiencies. American Journal of Engineering, Mechanics and Architecture, 2(11), 171-198. http://eprints.umsida.ac.id/16412/1/171-198%2BDriving%2BU.S.%2BBusiness%2BGrowth%2Bwith%2BAI-Driven%2BIntelligent%2BAutomation.pdf
13. Ankhi, R. B. (2025). Leveraging Business Intelligence and AI-Driven Analytics to Strengthen US Cybersecurity Infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(2), 9637-9652.
14. Inampudi, R. K., Kondaveeti, D., & Pichaimani, T. (2023). Optimizing Payment Reconciliation Using Machine Learning: Automating Transaction Matching and Dispute Resolution in Financial Systems. Journal of Artificial Intelligence Research, 3(1), 273-317.
15. Shao, X., & Li, L. (2011). Data-driven multi-touch attribution models. Proceedings of KDD 2011.
16. Tellis, G. J., & Tellis, K. (2009). Disaggregate models in marketing and advertising research: Actionable knowledge from aggregate data? International Journal of Research in Marketing, 26(4), 257–262.
17. Lilien, G. L. (1999). Marketing Engineering Applications. Addison-Wesley.
18. Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing, 68(1), 109–127.
19. Dekimpe, M. G., Franses, P. H., & Hanssens, D. M. (2008). Time-series models in marketing. In B. Wierenga (Ed.), Handbook of Marketing Decision Models (pp. 373–398). Springer.
20. Wimbledon, D. (2005). Saturation effects in media spend – modeling diminishing returns in advertising impact. Journal of Advertising Research, 45(3), 345–356.
21. Keller, K. L., & Lehmann, D. R. (2006). Brands and branding: Research findings and future priorities. Marketing Science, 25(6), 740–759.
22. Gahlot, S., Thangavelu, K., & Bhattacharyya, S. (2024). Digital Transformation in Federal Financial Aid: A Case Study of CARES Act Implementation through Low-Code Technologies. Newark Journal of Human-Centric AI and Robotics Interaction, 4, 15-45.
23. Gupta, S., & Lehmann, D. R. (2003). Customers as assets. Journal of Interactive Marketing, 17(1), 9–24.
24. Muthusamy, M. (2022). AI-Enhanced DevSecOps architecture for cloud-native banking secure distributed systems with deep neural networks and automated risk analytics. International Journal of Research Publication and Engineering Technology Management, 6(1), 7807–7813. https://doi.org/10.15662/IJRPETM.2022.0506014
25. Nagarajan, G. (2022). An integrated cloud and network-aware AI architecture for optimizing project prioritization in healthcare strategic portfolios. International Journal of Research and Applied Innovations, 5(1), 6444–6450. https://doi.org/10.15662/IJRAI.2022.0501004
26. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
27. Rust, R. T., & Hanssens, D. M. (1993). Modeling interindustry effects of advertising. Marketing Science, 12(1), 1–24.
