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Vol. 6 No. 1 (2025)
2025 Edition

Articles

AI-Driven Visual Element Optimization and Performance Prediction in Digital Advertising: An Empirical Analysis of Multi-Platform Campaign Effectiveness

Published 2025-12-25

  • Shakib Hassan

Shakib Hassan
University of Science & Technology Chittagong, Bangladesh

Abstract

Digital advertising has evolved into a data-intensive domain where visual elements play a critical role in campaign performance. This research investigates the application of artificial intelligence techniques in optimizing visual components and predicting advertising effectiveness across multiple digital platforms. Through comprehensive analysis of 847 advertising campaigns spanning social media, search engines, and display networks, this study examines the relationships between visual element characteristics and key performance indicators including click-through rates, conversion rates, and return on advertising spend. The research employs advanced analytical methods to identify optimal visual configurations and develop predictive frameworks for campaign outcomes. Empirical findings reveal significant variations in visual element effectiveness across different platforms and audience segments, with color scheme selection demonstrating 34.7% impact variance and compositional structure showing 28.3% performance differential. The study contributes actionable insights for advertising practitioners seeking to enhance campaign efficiency through data-driven visual optimization strategies while advancing theoretical understanding of visual communication effectiveness in digital environments.

Keywords

Digital advertising optimization, Visual element analysis, Performance prediction, Multi-platform campaigns

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