Customer lifetime value modeling and its use for customer retention planning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating Customer Value Considerations into Predictive Modeling
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
Analytics-driven solutions for customer targeting and sales-force allocation
IBM Systems Journal
Expert Systems with Applications: An International Journal
Toward a successful CRM: variable selection, sampling, and ensemble
Decision Support Systems
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
Uniformly subsampled ensemble (USE) for churn management: Theory and implementation
Expert Systems with Applications: An International Journal
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We consider prediction-model evaluation in the context of marketing-campaign planning. In order to evaluate and compare models with specific campaign objectives in mind, we need to concentrate our attention on the appropriate evaluation-criteria. These should portray the model's ability to score accurately and to identify the relevant target population. In this paper we discuss some applicable model-evaluation and selection criteria, their relevance for campaign planning, their robustness under changing population distributions, and their employment when constructing confidence intervals. We illustrate our results with a case study based on our experience from several projects.