Affiliations: [a] Faculty of Engineering and Applied Sciences, Universidad de los Andes, Las Condes, Santiago, Chile | [b] Department of Decision Sciences and Information Management, KU Leuven, Naamsestraat, Leuven, Belgium | [c] School of Management, University of Southampton, Highfield Southampton, UK | [d] Vlerick, Leuven-Gent Management School, Gent, Belgium
Correspondence:
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Corresponding author: Alex Seret, Faculty of Engineering and Applied Sciences, Universidad de los Andes, Las Condes, Santiago, Chile. E-mail:[email protected]
Abstract: The share of the services offered via the Internet by nowadays banking companies is quickly growing, making of the understanding of online customers one of the major concerns. Data mining tools have proven their efficiency in addressing this challenge by providing unsupervised quantitative techniques to identify those segments of customers with similar characteristics. This paper will focus on segmenting an online banking customer base in a meaningful way for the business by enhancing an unsupervised quantitative technique approach with domain knowledge. Both traditional and knowledge-based approaches will be applied and evaluated. Thanks to an extensive description and discussion of the new insights, the complementarity of the two approaches is illustrated.
Keywords: Data mining, constrained clustering, customer profiling, domain knowledge, direct marketing, banking
