Churn analysis using deep convolutional neural networks and autoencoders

18 Apr 2016Artit WangperawongCyrille BrunOlav LaudyRujikorn Pavasuthipaisit

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... (read more)

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