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