Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 7, June 2019

An Enhanced Ad Event-Prediction Method Based on Feature Engineering


Saeid Soheily-Khah and Yiming Wu, SKYLADS Research Team, France


In digital advertising, Click-Through Rate (CTR) and Conversion Rate (CVR) are very important metrics for evaluating ad performance. As a result, ad event prediction systems are vital and widely used for sponsored search and display advertising as well as Real-Time Bidding (RTB). In this work, we introduce an enhanced method for ad event prediction (i.e. clicks, conversions) by proposing a new efficient feature engineering approach. A large realworld event-based dataset of a running marketing campaign is used to evaluate the efficiency of the proposed prediction algorithm. The results illustrate the benefits of the proposed ad event prediction approach, which significantly outperforms the alternative ones.


Digital Advertising, Ad Event Prediction, Feature Engineering, Feature Selection, Statistical Test, Classification