# Churn analysis using deep convolutional neural networks and autoencoders

arXiv: https://arxiv.org/abs/1604.05377 · HTML: https://mlsanity.com/paper/1604.05377/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [Early Churn Prediction from Large Scale User-Product Interaction Time Series](https://mlsanity.com/paper/2309.14390/) (2023)
- [Challenges and opportunities in applying Neural Temporal Point Processes to large scale industry data](https://mlsanity.com/paper/2208.08623/) (2022)
- [A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction](https://mlsanity.com/paper/2308.08502/) (2023)

## Abstract (arXiv)

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.