Developing and Deploying a Churn Prediction Model with Azure Machine Learning Services - Developer Blog

Our sequential non-text information is best harnessed in a Bidirectional LSTM – a type of sequential model described in more detail here and here – that allows the model to learn end-of-sequence and beginning-of-sequence behavior. This maps to domain experts' knowledge that distinctive behavior at the end of the subscription period presages churn. It also captures the patterns in the progression of events over time that can be used to predict eventual churn. On the other hand our textual and categorical data need a separate model to learn from this differently structured data. We have several options here.