Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are built with the purpose of accurately predicting the probability of a customer d...

Nowadays, companies are investing in a well-considered CRM strategy. One of the cornerstones in CRM is customer churn prediction, where one tries to predict whether or not a customer will leave the company. This study focuses on how to better support marketing decision makers in identifying risky customers by using Generalized Additive Models (GAM). Compared to Logistic Regression, GAM relaxes ...

With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Timesensitive Customer Churn Prediction (TCCP) framework based on Positi...

Churn modeling is important to sustain profitable customer relationships in saturated consumer markets. A churn model predicts the likelihood of customer defection. This is important to target retention offers to the right customers and to use marketing resources efficiently. The prevailing approach toward churn model development, supervised learning, suffers an important limitation: it does no...

Customer churn is defined as the loss of customers because they move out to competitors. It is an expensive problem in many industries since acquiring new customers costs five to six times more than retaining existing ones [1-4]. In particular, in telecommunication companies, churn costs roughly $10 billion per year [5]. A wide range of supervised machine learning classifiers have been develope...

Customer churn may be a critical issue for banks. The extant literature on statistical and machine learning for customer churn focuses on the problem of correctly predicting that a customer is about to switch bank, while very rarely considers the problem of generating personalized actions to improve the customer retention rate. However, these decisions are at least as critical as the correct id...

Churn prediction is an important factor to consider for Customer Relationship Management (CRM). In this study, statistical and data mining techniques were used for churn prediction. We use linear (logistic regression) and non-linear techniques of Random Forest and Deep Learning architectures including Deep Neural Network, Deep Belief Networks and Recurrent Neural Networks for prediction. This i...

More and more literatures have researched the application of data mining technology in customer segmentation, and achieved sound effects. One of the key purposes of customer segmentation is customer retention. But the application of single data mining technology mentioned in previous literatures is unable to identify customer churn trend for adopting different actions on customer retention. Thi...

Customer churn is the term which indicates the customer who is in the stage to leave the company. Particularly it is happening recurrently in the telecommunication industry and the telecom industries are also in a position to retain their customer to avoid the revenue loss. Prediction of such behaviour is very vital for the present market and competition and Data mining is the one of the effect...

Owing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue customer churn was faced by telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring churn. Therefore, this work proposes a novel framework predict through deep learning namely Swish Recurrent Neural Network (S-RN...