  Off-the-shelf machine learning algorithms for prediction such as regularized
logistic regression cannot exploit the information of time-varying features
without previously using an aggregation procedure of such sequential data.
However, recurrent neural networks provide an alternative approach by which
time-varying features can be readily used for modeling. This paper assesses the
performance of neural networks for churn modeling using recency, frequency, and
monetary value data from a financial services provider. Results show that RFM
variables in combination with LSTM neural networks have larger top-decile lift
and expected maximum profit metrics than regularized logistic regression models
with commonly-used demographic variables. Moreover, we show that using the
fitted probabilities from the LSTM as feature in the logistic regression
increases the out-of-sample performance of the latter by 25 percent compared to
a model with only static features.
