  Churn prediction, or the task of identifying customers who are likely to
discontinue use of a service, is an important and lucrative concern of firms in
many different industries. As these firms collect an increasing amount of
large-scale, heterogeneous data on the characteristics and behaviors of
customers, new methods become possible for predicting churn. In this paper, we
present a unified analytic framework for detecting the early warning signs of
churn, and assigning a "Churn Score" to each customer that indicates the
likelihood that the particular individual will churn within a predefined amount
of time. This framework employs a brute force approach to feature engineering,
then winnows the set of relevant attributes via feature selection, before
feeding the final feature-set into a suite of supervised learning algorithms.
Using several terabytes of data from a large mobile phone network, our method
identifies several intuitive - and a few surprising - early warning signs of
churn, and our best model predicts whether a subscriber will churn with 89.4%
accuracy.
