Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments
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.
|Keywords||client segmentation, direct marketing, fuzzy clustering, fuzzy systems|
|Publisher||Erasmus Research Institute of Management (ERIM)|
Setnes, M., & Kaymak, U.. (2000). Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments (No. ERS-2000-49-LIS). Erasmus Research Institute of Management (ERIM). Retrieved from http://hdl.handle.net/1765/55