2Classification modeling methodology

2.1 An overview of the methodology for classification modeling

In this chapter, we present the methodological steps for classification modeling. Classification modeling is a form of supervised modeling in which the analytical task is to classify new instances in known classes. Historical data are used for model training. Classified cases are presented to the model which analyzes the data patterns and the associations of the input attributes (predictors) with the observed outcome. When the model is applied to new, unseen (unlabeled) cases, it assigns them to a class based on the predictors’ values and the identified data patterns associated with each class. Along with the class prediction, the model also estimates prediction confidence which denotes the likelihood of prediction. Many marketing applications can be “translated” to classification problems and be tackled with classification modeling, including optimization of targeted marketing campaigns for acquiring new customers, cross-/up-/deep-selling, and attrition prevention. Since the scope of this book is to be used as a guide for real analytical applications in marketing, in this chapter, we try to go beyond a generic presentation of the classification methodology. Therefore, we dedicate a large part of this chapter in explaining how classification modeling can be applied to support and optimize specific marketing applications in all major industries.

The following are the five main ...

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