The focus of this paper is to analyze the use of ensemble-based optimization for improvement of social media ad campaigns. The procedure of using more than one machine learning model yield very high possibilities in predictive accuracy, accuracy in targeting and most importantly in the issue of budgeting. Random Forest, Gradient Boosting & Stacking allow the advertisers to estimate the users’ behavior better – CTR, & fight Ad Fraud in a more effective manner. The paper supports the finding by providing three real-life use cases which illustrate The study provides examples for real-time bidding, audience segmentation, as well as sentiment analysis where the proposed approach outcompeted the baseline in terms of performance. It turns out that the use of ensemble learning in social media marketing can be helpful in more effective ad spend distribution, increasing the rates of engagement, and, as a result, the probability of successful campaigns will be higher.
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