Enhancing Ad Targeting through AI-Powered Audience Segmentation: Leveraging K-Means Clustering and Random Forest Algorithms

Abstract

This research paper explores the enhancement of ad targeting through advanced AI-powered audience segmentation, utilizing a combination of K-Means Clustering and Random Forest algorithms. The study addresses the growing need for precision in digital marketing by developing a robust methodology for segmenting audiences based on their behavioral and demographic data. The research begins by implementing K-Means Clustering to partition the audience into distinct groups according to shared characteristics, optimizing the selection of the number of clusters through the Elbow Method. Subsequently, the Random Forest algorithm is employed to refine these segments, offering insights into the variable importance and enhancing predictive accuracy of user conversion likelihood. Data was gathered from a large-scale digital marketing campaign comprising over 100,000 user profiles, ensuring diversity and comprehensiveness. The results indicate a significant improvement in targeting precision, with an increase in conversion rates by 15% compared to traditional segmentation methods. Moreover, the combined approach facilitates real-time adaptability to dynamic user behaviors and preferences. The findings demonstrate the potential of integrating machine learning techniques to revolutionize targeted advertising, offering marketers a sophisticated toolset for engaging their audience with unprecedented accuracy. Future research will focus on the integration of other machine learning models and exploring cross-sector applications, suggesting a promising trajectory for the development of intelligent ad targeting systems.