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Sponsored search advertising has become a successful channel for advertisers as well as a profitable business model for the leading commercial search engines. There is an extensive sponsored search research stream regarding the classification and prediction of performance metrics such as clickthrough rate, impression rate, average results page position and conversion rate. However, there is limited research on the application of advanced data mining techniques, such as ensemble learning, to pay per click campaign classification. This research presents an in-depth analysis of sponsored search advertising campaigns by comparing the classification results from four base classification models (Naïve Bayes, logistic regression, decision trees, and Support Vector Machines) with four popular ensemble learning techniques (Voting, Boot Strap Aggregation, Stacked Generalization, and MetaCost). The goal of our …