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Credit Card Marketing Case - Classification Trees

The Credit Card Marketing analytics case study.  The complete collection of analytics cases is available from Collection: Analytics Case Study Library.


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Key ideas:

Classification trees, validation, confusion matrix, misclassification, leaf report, ROC curves, lift curves.


A bank would like to understand the demographics and other characteristics associated with whether a customer accepts a credit card offer. Observational data is somewhat limited for this kind of problem, in that often the company sees only those who respond to an offer. To get around this, the bank designs a focused marketing study, with 18,000 current bank customers. This focused approach allows the bank to know who does and does not respond to the offer, and to use existing demographic data that is already available on each customer.

The designed approach also allows the bank to control for other potentially important factors so that the offer combination isn’t confused or confounded with the demographic factors. Because of the size of the data and the possibility that there are complex relationships between the response and the studied factors, a decision tree is used to find out if there is a smaller subset of factors that may be more important and that warrant further analysis and study.

The Task:

We want to build a model that will provide insight into why some bank customers accept credit card offers. Because the response is categorical (either Yes or No) and we have a large number of potential predictor variables, we use the Partition platform to build a classification tree for Offer Accepted. We are primarily interested in understanding characteristics of customers who have accepted an offer, so the resulting model will be exploratory in nature.



From Building Better Models with JMP® Pro, Chapter 6, SAS Press (2015). Grayson, Gardner and Stephens. Used with permission.