Integrating customer value considerations into predictive modeling

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 customers. We discuss, both theoretically and empirically, the optimal use of "customer value" data in the model training, model evaluation and scoring stages. Our main conclusion is that a non-trivial approach of using "decayed" value-weights for training is usually preferable to the two obvious approaches of either using non-decayed customer values as weights or ignoring them.