Integrating Secure Data Governance and Scalable Analytics for Intelligent Marketing

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 376-385, March 2026.

https://doi.org/10.58482/ijersem.v2i3.48

This work is licensed under a Creative Commons Attribution 4.0 International License.

Integrating Secure Data Governance and Scalable Analytics for Intelligent Marketing

R G Kumar

M Pujitha

F Hussain Abbas

K S Kishore Kumar

N K Mohanamithran

1Associate Professor, Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, AP, India.

2-5UG Scholar, Department of CSE, Siddharth Institute of Engineering & Technology, Puttur, AP, India.

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Abstract: An intelligent and secure data-driven framework for online marketing that integrates data governance, scalable analytics, and AI-based decision support to enhance campaign performance. The proposed system begins with structured data collection and ingestion from multiple digital advertising sources, followed by strict GDPR-compliant data governance and privacy enforcement to ensure secure and ethical data handling. A comprehensive data preprocessing and quality assurance module removes inconsistencies, cleans outliers, and prepares high-quality data for analysis. The analytical core employs machine learning models, specifically Random Forest and Gradient Boosting, to predict campaign performance and optimize marketing strategies. Experimental results show that the Gradient Boosting model achieved a high prediction accuracy of 98.99%, outperforming traditional models. The system analyzed 4,454 marketing campaigns, of which 3,186 campaigns (71.5%) were successful, demonstrating strong predictive reliability. Financial analysis revealed a total advertising spend of $24.8 million, generating 1,049,598 conversions with an average ROI of 171.83%, highlighting the effectiveness of the proposed optimization strategy. Engagement analysis further indicated an average click-through rate (CTR) of 0.0797 and an average session duration of 311 seconds, reflecting strong user interaction and content relevance. The framework also integrates real-time dashboards for campaign monitoring and a feedback loop for continuous optimization, enabling dynamic budget reallocation, performance tracking, and data-driven decision-making. Overall, the proposed system successfully combines secure data governance, scalable AI analytics, and intelligent feedback mechanisms to deliver a robust, efficient, and privacy-aware marketing solution, making it highly suitable for modern digital advertising environment.

Keywords: Secure Data Governance, Intelligent Marketing, Machine Learning, Gradient Boosting, Campaign Analytics.

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This article is part of Volume 2, Issue 3 (March 2026)