  Customer temporal behavioral data was represented as images in order to
perform churn prediction by leveraging deep learning architectures prominent in
image classification. Supervised learning was performed on labeled data of over
6 million customers using deep convolutional neural networks, which achieved an
AUC of 0.743 on the test dataset using no more than 12 temporal features for
each customer. Unsupervised learning was conducted using autoencoders to better
understand the reasons for customer churn. Images that maximally activate the
hidden units of an autoencoder trained with churned customers reveal ample
opportunities for action to be taken to prevent churn among strong data, no
voice users.
