  Accurately predicting customer churn using large scale time-series data is a
common problem facing many business domains. The creation of model features
across various time windows for training and testing can be particularly
challenging due to temporal issues common to time-series data. In this paper,
we will explore the application of extreme gradient boosting (XGBoost) on a
customer dataset with a wide-variety of temporal features in order to create a
highly-accurate customer churn model. In particular, we describe an effective
method for handling temporally sensitive feature engineering. The proposed
model was submitted in the WSDM Cup 2018 Churn Challenge and achieved
first-place out of 575 teams.
