Mastering Data Mining: The Art and Science of Customer Relationship Management
Mastering Data Mining: The Art and Science of Customer Relationship Management
Comparing and aggregating rankings with ties
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Counting positives accurately despite inaccurate classification
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Frequently, organisations have to face complex situations where decision making is difficult. In these scenarios, several related decisions must be made at a time, which are also bounded by constraints (e.g. inventory/stock limitations, costs, limited resources, time schedules, etc). In this paper, we present a new method to make a good global decision when we have such a complex environment with several local interwoven data mining models. In these situations, the best local cutoff for each model is not usually the best cutoff in global terms. We use simulation with Petri nets to obtain better cutoffs for the data mining models. We apply our approach to a frequent problem in customer relationship management (CRM), more specifically, a direct-marketing campaign design where several alternative products have to be offered to the same house list of customers and with usual inventory limitations. We experimentally compare two different methods to obtain the cutoff for the models (one based on merging the prospective customer lists and using the local cutoffs, and the other based on simulation), illustrating that methods which use simulation to adjust model cutoff obtain better results than a more classical analytical method.