Evaluation of prediction models for marketing campaigns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Knowledge Discovery
Unsupervised Profiling for Identifying Superimposed Fraud
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
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
Championing of an LTV model at LTC
ACM SIGKDD Explorations Newsletter
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The success of prediction models for business purposesshould not be measured by their accuracy only. Theirevaluation should also take into account the higherimportance of precise prediction for "valuable"customers. We illustrate this idea through the example ofchurn modeling in telecommunications, where it isobviously much more important to identify potentialchurn among valuable customers. We discuss, boththeoretically and empirically, the optimal use of"customer value" data in the model training, modelevaluation and scoring stages. Our main conclusion isthat a non-trivial approach of using "decayed" value-weightsfor training is usually preferable to the twoobvious approaches of either using non-decayed customervalues as weights or ignoring them.