# Churn analysis using deep convolutional neural networks and autoencoders

Source: [Churn analysis using deep convolutional neural networks and autoencoders](<https://www.sota2.com/research/paper/68fa62641e209131ce9f90cd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:18:31.734Z

Authors: Artit Wangperawong, Cyrille Brun, Olav Laudy, Rujikorn Pavasuthipaisit

Published: 2016-04-18T23:18:23.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1604.05377>)

[PDF](<https://arxiv.org/pdf/1604.05377.pdf>)