  In this work, we presented the strategies and techniques that we have
developed for predicting the near-future churners and win-backs for a telecom
company. On a large-scale and real-world database containing customer profiles
and some transaction data from a telecom company, we first analyzed the data
schema, developed feature computation strategies and then extracted a large set
of relevant features that can be associated with the customer churning and
returning behaviors. Our features include both the original driver factors as
well as some derived features. We evaluated our features on the imbalance
corrected dataset, i.e. under-sampled dataset and compare a large number of
existing machine learning tools, especially decision tree-based classifiers,
for predicting the churners and win-backs. In general, we find RandomForest and
SimpleCart learning algorithms generally perform well and tend to provide us
with highly competitive prediction performance. Among the top-15 driver factors
that signal the churn behavior, we find that the service utilization, e.g. last
two months' download and upload volume, last three months' average upload and
download, and the payment related factors are the most indicative features for
predicting if churn will happen soon. Such features can collectively tell
discrepancies between the service plans, payments and the dynamically changing
utilization needs of the customers. Our proposed features and their
computational strategy exhibit reasonable precision performance to predict
churn behavior in near future.
