# Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors

arXiv: https://arxiv.org/abs/1703.03869 · HTML: https://mlsanity.com/paper/1703.03869/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2017.

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

- [Multimodal Deep Learning of Word-of-Mouth Text and Demographics to Predict Customer Rating: Handling Consumer Heterogeneity in Marketing](https://mlsanity.com/paper/2401.11888/) (2024)
- [Modelling customer churn for the retail industry in a deep learning based sequential framework](https://mlsanity.com/paper/2304.00575/) (2023)
- [Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions](https://mlsanity.com/paper/2304.11503/) (2023)

## Abstract (arXiv)

As companies increase their efforts in retaining customers, being able to predict accurately ahead of time, whether a customer will churn in the foreseeable future is an extremely powerful tool for any marketing team. The paper describes in depth the application of Deep Learning in the problem of churn prediction. Using abstract feature vectors, that can generated on any subscription based company's user event logs, the paper proves that through the use of the intrinsic property of Deep Neural Networks (learning secondary features in an unsupervised manner), the complete pipeline can be applied to any subscription based company with extremely good churn predictive performance. Furthermore the research documented in the paper was performed for Framed Data (a company that sells churn prediction as a service for other companies) in conjunction with the Data Science Institute at Lancaster University, UK. This paper is the intellectual property of Framed Data.