Customer churn is one of the most challenging problems that affects revenue and customer
base in mobile telecom operators. The success of retention campaigns depends not only on …

Deep neural network (DNN) architectures such as recurrent neural networks and
transformers display outstanding performance in modeling sequential unstructured data …

Most widespread churn prediction models assume customer independence, ignoring the
well-documented propagation of churn influence in a customer network. Although this …

Off-the-shelf machine learning algorithms for prediction such as regularized logistic
regression cannot exploit the information of time-varying features without previously using …

Currently, Customer churn is a major challenge for e-commerce companies. It is necessary
to have customer churn prediction model for e-commerce companies to predict the customer …

For brands, gaining new customer is more expensive than keeping an existing one.
Therefore, the ability to keep customers in a brand is becoming more challenging these …

In this paper, we investigated the customer churn prediction problem in the Internet funds
industry. We designed a novel feature embedded convolutional neural networks (FE-CNN) …

Churn prediction, which is to identify who are prone to abandon the subscription, is of high
significance for the operators to retain the potential churners. It should be noted that, in …

Telecom companies require the accurate prediction of probable churn customers to
improvise the customer relationship management; this is addressed through customer churn …

The significance of customer churn analysis has escalated due to the increasing availability
of relevant data and intensifying competition. Researchers and practitioners are focused on …