Machine Learning Models for Predicting Click-through Rates on social media: Factors and Performance Analysis

Abstract

In today's digital landscape, social media has become an integral component of marketing strategies, making the prediction of Click-through Rates (CTR) a critical endeavor for businesses aiming to maximize their online presence and reach their target audience effectively. This research article embarks on a comprehensive exploration of the application of machine learning models in the context of CTR prediction within the realm of social media platforms. As the digital marketing landscape evolves, so too does the complexity of factors influence user engagement and CTR on social media platforms. Understanding and harnessing these factors is paramount for marketers seeking to thrive in this dynamic environment. This study, therefore, takes on the crucial task of dissecting the multifaceted determinants of CTR, encompassing elements such as ad content, user demographics, timing, and engagement metrics. Through rigorous analysis and data-driven insights, it sheds light on how these factors interplay and impact CTR outcomes, providing a roadmap for marketers to tailor their strategies more effectively. Moreover, this research scrutinizes the performance of various machine learning models in predicting CTR, offering a comparative analysis of their strengths and weaknesses. It navigates the landscape of model selection and employs an array of evaluation metrics to gauge their efficacy. The results not only provide a comprehensive understanding of which models are most suited for CTR prediction on social media but also illuminate the importance of refining data preprocessing techniques and feature engineering in enhancing model accuracy.