Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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Decision Support Systems
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
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Building Data Mining Applications for CRM
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Data Mining Techniques: For Marketing, Sales, and Customer Support
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Data Mining: Concepts, Models, Methods and Algorithms
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Optimization-based feature selection with adaptive instance sampling
Computers and Operations Research
TreeLogit Model for Customer Churn Prediction
APSCC '06 Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Toward a hybrid data mining model for customer retention
Knowledge-Based Systems
Searching customer patterns of mobile service using clustering and quantitative association rule
Expert Systems with Applications: An International Journal
ISDPE '07 Proceedings of the The First International Symposium on Data, Privacy, and E-Commerce
Expert Systems with Applications: An International Journal
Determinants of intangible assets value: The data mining approach
Knowledge-Based Systems
Sequential manifold learning for efficient churn prediction
Expert Systems with Applications: An International Journal
Mobile phone customer retention strategies and Chinese e-commerce
Electronic Commerce Research and Applications
Hi-index | 12.05 |
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 prediction models, such as neural networks and decision trees in the domain of mobile telecommunication. However, much related work focuses on developing the prediction models per se. Few studies consider the pre-processing step during data mining whose aim is to filter out unrepresentative data or information. This paper presents the important processes of developing MOD customer churn prediction models by data mining techniques. They contain the pre-processing stage for selecting important variables by association rules, which have not been applied before, the model construction stage by neural networks (NN) and decision trees (DT), which are widely adapted in the literature, and four evaluation measures including prediction accuracy, precision, recall, and F-measure, all of which have not been considered to examine the model performance. The source data are based on one telecommunication company providing the MOD services in Taiwan, and the experimental results show that using association rules allows the DT and NN models to provide better prediction performances over a chosen validation dataset. In particular, the DT model performs better than the NN model. Moreover, some useful and important rules in the DT model, which show the factors affecting a high proportion of customer churn, are also discussed for the marketing and managerial purpose.