Machine learning-based classification of the traffic of digital marketing campaigns

Digital marketing i.e., any type of advertising proposed on electronic devices over the Internet, has been the prevalent advertising methodology in the last decades. As such, measuring its effectiveness and distinguishing genuine conversions generated by real users from fraudulent traffic (for example generated by bots) is of utmost importance. In this paper, such issue is tackled by using Machine Learning and, specifically, comparing a Shallow Neural Network and a Random Forest, to automatically classify traffic data of digital marketing campaigns into 'good' and 'bad' traffic. The two proposed models are evaluated on real traffic data, manually annotated. The results demonstrate the feasibility of the proposed classification task, with the Shallow Neural Network achieving a 98% precision and recall, without exhibiting overfitting, over the 32,000 samples of the tested dataset.