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

TL;DR

This paper demonstrates that deep learning, specifically unsupervised feature learning with neural networks, can effectively predict customer churn across various subscription-based companies using abstract feature vectors derived from user logs.

Contribution

The paper introduces a universal deep learning pipeline for churn prediction that works with abstract, company-independent feature vectors, enabling broad applicability and high accuracy.

Findings

High predictive performance across multiple companies

Effective unsupervised feature learning with deep neural networks

Applicable to any subscription-based company's user logs

Abstract

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.