Chapter 7. Case Study: Modeling Response to Direct Mail Marketing

  1. Daniel T. Larose Ph.D. Director

Published Online: 30 JAN 2006

DOI: 10.1002/0471756482.ch7

Data Mining Methods and Models

Data Mining Methods and Models

How to Cite

Larose, D. T. (2005) Case Study: Modeling Response to Direct Mail Marketing, in Data Mining Methods and Models, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471756482.ch7

Author Information

  1. Department of Mathematical Sciences, Central Connecticut State University, USA

Publication History

  1. Published Online: 30 JAN 2006
  2. Published Print: 11 NOV 2005

ISBN Information

Print ISBN: 9780471666561

Online ISBN: 9780471756484



  • CRISP-DM standard process for data mining;
  • BIRCH clustering algorithm;
  • over-balancing;
  • misclassification costs;
  • cost / benefit analysis;
  • model combination;
  • voting;
  • mean response probabilities


The case study begins with an overview of the cross-industry standard process for data mining: CRISP-DM. For the business understanding phase, the direct mail marketing response problem is defined, with particular emphasis on the construction of an accurate cost / benefit table, which will be used to assess the usefulness of all later models. In the data understand and data preparation phases, the Clothing Store data set is explored. Transformations to achieve normality or symmetry are applied, as is standardization and the construction of flag variables. Useful new variables are derived. The relationships between the predictors and the response are explored, and the correlation structure among the predictors is investigated. Next comes the modeling phase. Here, two principal components are derived, using principal components analysis. Clustering analysis is performed, using the BIRCH clustering algorithm. Emphasis is laid on the effects of balancing (and over-balancing) the training data set. The baseline model performance is established. Two sets of models are examined, Collection A, which uses the principal components, and Collection B, which does not. The technique of using over-balancing as a surrogate for misclassification costs is applied. The method of combining models via voting is demonstrated, as is the method of combining models using the mean response probabilities.