AI-Based Analysis of Colour Balance Elements in Printed Advertisements a Case Study of Zain Iraq Ads

The current research examines the impact of quantitative parameters of colors on printed advertisements using Support Vector Machines (SVM) models, particularly which focused on Zain Iraq advertisements. The impact of using color on advertisement engagement, emotional responses, and brand recognition is critical, however traditional evaluations are mainly subjective. The study aimed to address this gap using volunteered big data as well as sophisticated AI technologies like computer vision and machine learning apps to offer neutral assessments of how various color combinations perform. It examines a sample of 500 ads posted by Zain Iraq (2019-2024) which are classified into warm, cool, and neutral color palettes to compare the viewer's engagement and emotional response to various color combinations. Red and yellow elicit a more powerful emotional response and a higher level of viewer engagement than blue and green. We also found that advertisements with a combination of warm and cool colors led to the highest overall engagement rates, while neutral color usage was linked to decreased engagement. This study is also significant, as it illustrates the potential for AI based analytical procedures to enhance ad strategies through empirical data, thus creating an amplified impact of visual communication.