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Customer temporal behavioral data was represented as images in order toperform churn prediction by leveraging deep learning architectures prominent inimage classification. Supervised learning was performed on labeled data of over6 million customers using deep convolutional neural networks, which achieved anAUC of 0.743 on the test dataset using no more than 12 temporal features foreach customer. Unsupervised learning was conducted using autoencoders to betterunderstand the reasons for customer churn. Images that maximally activate thehidden units of an autoencoder trained with churned customers reveal ampleopportunities for action to be taken to prevent churn among strong data, novoice users.

Churn analysis using deep convolutional neural networks and autoencoders