# An Enhanced Ad Event-Prediction Method Based on Feature Engineering

## TL;DR

This paper presents an improved ad event prediction method utilizing a novel feature engineering approach, demonstrating significant performance gains on real-world marketing data for CTR and CVR prediction.

## Contribution

The paper introduces a new feature engineering technique that enhances the accuracy of ad event prediction models in digital advertising.

## Key findings

- Significant improvement over existing prediction methods
- Effective on large real-world marketing datasets
- Enhanced prediction accuracy for CTR and CVR

## Abstract

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.