Neural Networks for Target Selection in Direct Marketing Rob Potharst, Uzay Kaymak, Wim Pijls Erasmus Research Institute of Management Report Series Research in Management Bibliographic Data and Classifications

Partly due to a growing interest in direct marketing, it has become an important application field for data mining. Many techniques have been applied to select the targets in commercial applications, such as statistical regression, regression trees, neural computing, fuzzy clustering and association rules. Modeling of charity donations has also recently been considered. The availability of a large number of techniques for analyzing the data may look overwhelming and ultimately unnecessary at first. However, the amount of data used in direct marketing is tremendous. Further, there are different types of data and likely strong nonlinear relations amongst different groups within the data. Therefore, it is unlikely that there will be a single method that can be used under all circumstances. For that reason, it is important to have access to a range of different target selection methods that can be used in a complementary fashion. In this respect, learning systems such as neural networks have the advantage that they can adapt to the nonlinearity in the data to capture the complex relations. This is an important motivation for applying neural networks for target selection. In this report, neural networks are applied to target selection in modeling of charity donations. Various stages of model building are described by using data from a large Dutch charity organization as a case. The results are compared with the results of more traditional methods for target selection such as logistic regression and CHAID. 5001-6182 Business 5201-5982 Business Science Library of Congress Classification (LCC) HF 5415.126 Direct Marketing M Business Administration and Business Economics M 11 R 4 Production Management Transportation Systems Journal of Economic Literature (JEL) M 31 C 45 Marketing Neural Networks and Related Topics 85 A Business General 260 K 240 B Logistics Information Systems Management European Business Schools Library Group (EBSLG) 280 K Marketing Science (quantitative analysis) Gemeenschappelijke Onderwerpsontsluiting (GOO) 85.00 Bedrijfskunde, Organisatiekunde: algemeen 85.34 85.20 Logistiek management Bestuurlijke informatie, informatieverzorging Classification GOO 85.40 54.72 Marketing Kunstmatige Intelligentie Bedrijfskunde / Bedrijfseconomie Bedrijfsprocessen, logistiek, management informatiesystemen