Cost-Sensitive-Data Preprocessing for Mining Customer Relationship Management Databases

A staged-framework for data preprocessing has been developed to support data mining and help service providers identify customers who might switch to a competitor. The framework pushes the cost sensitivity and data imbalance of customer retention data into the data preprocessing itself. Tests using data set from the ACM KDD Cup 1998 showed that the framework outperformed the winner of that data mining and knowledge discovery competition. The framework has also been incorporated into a software system, called ED-Money. To demonstrate the framework's ability to predict customer attrition with high accuracy, it was applied to some benchmark data and to a real customer attrition data set from a large Chinese mobile telecommunications company

Authors

• Junfeng Pan Lei Li
• Qiang Yang Hong Kong University of Science and Technology, Hong Kong SAR, China
• Yiming Yang Carnegie Mellon University
• Lei Li Cleveland State University
• Frances Li
• George Li

Keywords

• Customer relationship management
• Databases
• Costs
• Data preprocessing
• Data mining
• Switches
• Sun
• Communication industry
• Industrial relations
• Mining industry
• Data Pre-processing
• Mobile Communication
• Cost Information
• Customer Value
• Direct Marketing
• Neural Network
• Training Data
• Decision Tree
• Response Ratio
• Training Subsets
• Test Subset
• Net Profit
• Linearly Separable
• Ensemble Of Networks
• Validation Subset
• Cost Matrix
• Earn Money
• Back Propagation Neural Network
• Back-propagation Network
• cost-sensitive-data preprocessing
• ensemble of classifiers