Evaluating Deep Learning in Churn Prediction for Everything-as-a-Service in the Cloud
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© 2017 IEEE. Cloud computing has seen rapid growth due to its massive scalability in storage and computing power. Leading the trend, IBM released a hybrid cloud development platform, based on infrastructure as a service. Although tens of thousands of customers visit the platform everyday, a large percentage of trial customers left as their free-trial access expired, and a high proportion of paying customers dropped their usage sharply or worse stopped their payment. Customer retention in marketing is critical for reduced cost in retaining temporary customers and higher profits from long-term customers. In this paper, we present a data-driven iterative churn prediction framework with a deep learning approach for everything as a service (XaaS) in the cloud, including a cloud platform or software. Moreover, to improve churn prediction analysis we propose a new temporal customer engagement analysis model, called Nascency, Intermediate, and Latest (NIL) analysis. The NIL analysis helps to capture the temporal changes from time-related usage features, obtaining the three standardized inputs from instances which have different lifetimes. To the best of our knowledge this is the first study to use deep learning for churn prediction with time-correlation features in cloud computing. Our experiments have revealed strengths and weaknesses of deep learning in churn prediction. We expect these insights to help us improve deep learning's performance in churn prediction, by exploiting its hierarchical abstraction capability.
author list (cited authors)
Sung, C., Higgins, C. Y., Zhang, B. o., & Choe, Y.