Multimedia on demand (MOD) is an interactive system that provides a number of value-added services in addition to traditional TV services, such as video on demand and interactive online learning. This opens a new marketing and managerial problem for the telecommunication industry to retain valuable MOD customers. Data mining techniques have been widely applied to develop customer churn predicti...

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...

Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. Please do not copy or distribute without explicit permission of the authors. Abstract Customer defection or churn is a widespread phenomenon that threatens fir...

The credit card customer churn rate is the percentage of a bank’s customers that stop using services. Hence, developing prediction model to predict expected status for will generate an early alert banks change service or offer them new This paper aims develop by feature-selection method and five machine learning models. To select independent variables, three models were used, including selectio...

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...

The success of prediction models for business purposes should not be measured by their accuracy only. Their evaluation should also take into account the higher importance of precise prediction for “valuable” customers. We illustrate this idea through the example of churn modeling in telecommunications, where it is obviously much more important to identify potential churn among valuable customer...