Real-Time Predictive Optimization Framework for Multi-Platform Digital Advertising Using Machine Learning and Multi-Criteria Decision Analysis

Abstract

The rapid growth of digital advertising across search engines, social media platforms, video-sharing services, and mobile applications has significantly increased the complexity of campaign management and advertisement optimization. Modern advertising ecosystems generate large volumes of real-time behavioral data, requiring intelligent decision-support mechanisms capable of continuously adapting advertisement placement, budget allocation, and platform selection. Conventional optimization approaches predominantly rely on static bidding strategies or rule-based decision models that often fail to respond effectively to rapidly changing user behavior and dynamic market conditions. Although machine learning has demonstrated considerable success in predicting campaign performance, existing studies rarely integrate predictive analytics with multi-criteria decisionmaking to support real-time advertising optimization across multiple digital platforms. This paper proposes an AI-Driven Real-Time Predictive Optimization Framework (AIPOF) that integrates machine learning- based performance prediction with Multi-Criteria Decision Analysis (MCDA) to improve multi-platform digital advertising decisions. The proposed framework employs predictive analytics to estimate campaign performance using key advertising indicators, including targeting accuracy, real-time responsiveness, platform compatibility, and cost efficiency. These predicted performance measures are subsequently incorporated into a Complex Proportional Assessment (COPRAS) model to rank alternative advertising strategies and identify the most effective campaign configuration under multiple competing objectives. The proposed framework is evaluated through a comparative analysis of five representative digital advertising strategies using standardized performance criteria and objective weighting mechanisms. Experimental results demonstrate that integrating predictive analytics with multi-criteria decision analysis enables more balanced and informed advertising decisions by simultaneously considering campaign effectiveness, computational responsiveness, cross-platform adaptability, and advertising cost. The comparative evaluation further indicates that AI-assisted optimization provides superior decision consistency and supports efficient resource allocation in dynamic advertising environments. The proposed AIPOF framework contributes to intelligent digital marketing by bridging the gap between predictive machine learning and decision optimization within real-time advertising ecosystems. The framework offers a practical decision-support solution that can assist marketing professionals, advertising platforms, and campaign managers in improving return on investment, optimizing advertising expenditure, and enhancing campaign performance across heterogeneous digital platforms. Furthermore, the proposed methodology establishes a scalable foundation for future AI-driven autonomous advertising systems capable of continuous learning and adaptive optimization within modern digital marketing environments. Keywords: digital advertising; campaign optimization; machine learning; predictive analytics; multi-criteria decision analysis; COPRAS; programmatic advertising; real-time bidding; decision support