Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence (CI) techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. This paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing (DM) purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection.

client segmentation, direct marketing, fuzzy clustering, fuzzy systems
Data Collection and Data Estimation Methodology; Computer Programs (jel C8), Business Administration and Business Economics; Marketing; Accounting (jel M), Production Management (jel M11), Marketing (jel M31), Transportation Systems (jel R4)
Erasmus Research Institute of Management
hdl.handle.net/1765/55
ERIM Report Series Research in Management
Copyright 2000, M. Setnes, U. Kaymak, This report in the ERIM Report Series Research in Management is intended as a means to communicate the results of recent research to academic colleagues and other interested parties. All reports are considered as preliminary and subject to possibly major revisions. This applies equally to opinions expressed, theories developed, and data used. Therefore, comments and suggestions are welcome and should be directed to the authors.
Erasmus Research Institute of Management

Setnes, M, & Kaymak, U. (2000). Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments (No. ERS-2000-49-LIS). ERIM Report Series Research in Management. Erasmus Research Institute of Management. Retrieved from http://hdl.handle.net/1765/55