Customer Churn is one of the most important and challenging problems for businesses like banks, SAAS or telecommunication companies. Churn is expensive for the business since it costs more to acquire new customers than it does to retain the existing ones. Stakeholders invest a lot of time and effort in finding out how they can accurately distinguish existing customers that are about to leave. The answer to this question allows teams to take action. Most of the churn analysis approaches focus on predicting which customers are about to churn.

To whom correspondence should be addressed; Email: artitw@gmail.com 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.