# Modelling customer churn for the retail industry in a deep learning based sequential framework

arXiv: https://arxiv.org/abs/2304.00575 · HTML: https://mlsanity.com/paper/2304.00575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 2 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks](https://mlsanity.com/paper/2003.01176/) (2020)
- [Deep Recurrent Survival Analysis](https://mlsanity.com/paper/1809.02403/) (2018)
- [Adversarial Time-to-Event Modeling](https://mlsanity.com/paper/1804.03184/) (2018)
- [On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis](https://mlsanity.com/paper/1707.01780/) (2017)
- [Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis](https://mlsanity.com/paper/2002.09358/) (2020)
- [Deep Learning in Customer Churn Prediction: Unsupervised Feature Learning on Abstract Company Independent Feature Vectors](https://mlsanity.com/paper/1703.03869/) (2017)
- [Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand Forecasting](https://mlsanity.com/paper/1812.11444/) (2018)
- [Nonlinear Semi-Parametric Models for Survival Analysis](https://mlsanity.com/paper/1905.05865/) (2019)
- [A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services](https://mlsanity.com/paper/1309.3945/) (2013)
- [Optimal survival trees ensemble](https://mlsanity.com/paper/2005.09043/) (2020)

## Built on by (most cited first, 1 of 1)

- [SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference](https://mlsanity.com/paper/2605.15488/) (2026)

## Abstract (arXiv)

As retailers around the world increase efforts in developing targeted marketing campaigns for different audiences, predicting accurately which customers are most likely to churn ahead of time is crucial for marketing teams in order to increase business profits. This work presents a deep survival framework to predict which customers are at risk of stopping to purchase with retail companies in non-contractual settings. By leveraging the survival model parameters to be learnt by recurrent neural networks, we are able to obtain individual level survival models for purchasing behaviour based only on individual customer behaviour and avoid time-consuming feature engineering processes usually done when training machine learning models.