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 comp...

Churn analysis can be understood as a problem of predicting and understanding abandonment of use of a product or service. Di erent industries ranging from entertainment to financial investment, and cloud providers make use of digital platforms where their users access their product offerings. Usage often leads to behavioural trails being left behind. These trails can then be mined to understand...

Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (‘churn’) at their convenience and without notification. This makes the task of predicting churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying on three dimensions: data, outcome definition and popula...

Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (‘churn’) at their convenience and without notification. This makes the task of predicting prepaid churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying the experimental setup on three dimensions: data...

With the development of telecom business, customer churn prediction becomes more and more important. An outstanding issue in customer churn prediction is high dimensional problem. Curse of dimensionality will easily occur if effective feature extraction is not applied during modeling. Among the most popular feature extraction approaches, principal component analysis (PCA) method based on induct...

With the onset of social media and news aggregators on the Web, the newspaper industry is faced with a declining subscriber base. In order to retain customers both on-line and in print, it is therefore critical to predict and mitigate customer churn. Newspapers typically have heterogeneous sources of valuable data: circulation data, customer subscription information, news content, and search cl...

Customer churn prediction is one of the most important problems in customer relationship management (CRM). Its aim is to retain valuable customers to maximize the profit of a company. To predict whether a customer will be a churner or non-churner, there are a number of data mining techniques applied for churn prediction, such as artificial neural networks, decision trees, and support vector mac...

Churn prediction methods are widely used in Customer Relationship Management and have proven to be valuable for retaining customers. To obtain a high predictive performance, recent studies rely on increasingly complex machine learning methods, such as ensemble or hybrid models. However, the more complex a model is, the more difficult it becomes to understand how decisions are actually made. Pre...

In this paper, we propose a churn management model based on a partial least square (PLS) optimization method that explicitly considers the management costs of controllable marketing variables for a successful churn management program. A PLS prediction model is first calibrated to estimate the churn probabilities of customers. Then this PLS prediction model is transformed into a control model af...

In this paper, we present a cohort-based classification approach to the churn prediction for social on-line games. The original metric is proposed and tested on real data showing a good increase in revenue by churn preventing. The core of the approach contains such components as tree-based ensemble classifiers and threshold optimization by decision boundary.