A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction

In this paper, we perform an exploratory study of user Conversion Rate (CVR) prediction using recent big data from a global mobile marketing company. We design a stream processing engine to collect sampled mobile marketing data. Then, we execute a large set of CVR prediction tests, under a two-stage experimental procedure that considers a rolling window evaluation. First, several preprocessing and machine learning combinations are analyzed using preliminary data. Next, the selected combinations are tested on a larger set of unseen datasets. Interesting classification performances were achieved, with some learning models (e.g., XGboost, Logistic Regression) requiring a reduced computational effort, thus showing a potential value for user CVR prediction in this domain.