# Churn Prediction with Sequential Data and Deep Neural Networks. A Comparative Analysis

arXiv: https://arxiv.org/abs/1909.11114 · HTML: https://mlsanity.com/paper/1909.11114/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [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)
- [Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation](https://mlsanity.com/paper/2109.11871/) (2021)
- [Challenges and opportunities in applying Neural Temporal Point Processes to large scale industry data](https://mlsanity.com/paper/2208.08623/) (2022)
- [CLARGA: Multimodal Graph Representation Learning over Arbitrary Sets of Modalities](https://mlsanity.com/paper/2512.11901/) (2025)

## Abstract (arXiv)

Off-the-shelf machine learning algorithms for prediction such as regularized logistic regression cannot exploit the information of time-varying features without previously using an aggregation procedure of such sequential data. However, recurrent neural networks provide an alternative approach by which time-varying features can be readily used for modeling. This paper assesses the performance of neural networks for churn modeling using recency, frequency, and monetary value data from a financial services provider. Results show that RFM variables in combination with LSTM neural networks have larger top-decile lift and expected maximum profit metrics than regularized logistic regression models with commonly-used demographic variables. Moreover, we show that using the fitted probabilities from the LSTM as feature in the logistic regression increases the out-of-sample performance of the latter by 25 percent compared to a model with only static features.