  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 real-world 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.
