  A practical churn customer prediction model is critical to retain customers
for telecom companies in the saturated and competitive market. Previous studies
focus on predicting churn customers in current or next month, in which telecom
companies don't have enough time to develop and carry out churn management
strategies. In this paper, we propose a new T+2 churn customer prediction
model, in which the churn customers in two months are recognized and the
one-month window T+1 is reserved to carry out churn management strategies.
However, the predictions for churn customers in two months are much more
difficult than in current or next month because of the weaker correlation
between the customer information and churn states. Two characteristics of
telecom dataset, the discrimination between churn and non-churn customers is
complicated and the class imbalance problem is serious, are observed. To
discriminate the churn customers accurately, random forest (RF) classifier is
chosen because RF solves the nonlinear separable problem with low bias and low
variance and handles high feature spaces and large number of training examples.
To overcome the imbalance problem, synthetic minority over-sampling with
borderline or tomek link, in which the distribution of the samples remains and
the number of the training examples becomes larger, is applied. Overall, a
precision ratio of about 50% with a recall ratio of about 50% is achieved in
the T+2 churn prediction. The proposed prediction model provides an accurate
and operable churn customer prediction model for telecom companies.
