Fuzzy Classification of Online Customers

Nicolas Werro

Feb. 2015 · Springer

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This book introduces a fuzzy classification approach, which combines relational databases with fuzzy logic for more effective and powerful customer relationship management (CRM). It shows the benefits of a fuzzy classification in contrast to the traditional sharp evaluation of customers for the acquisition, retention and recovery of customers in online shops. The book starts with a presentation of the basic concepts, fuzzy set theory and the combination of relational databases and fuzzy classification. In its second part, it focuses on the customer perspective, detailing the central concepts of CRM, its theoretical constructs and aspects of analytical, operational and collaborative CRM. It juxtaposes fuzzy and sharp customer classes and shows the implications for customer positioning, mass customization, personalization, customer assessment and controlling. Finally, the book presents the application and implementation of the concepts in online shops. A detailed case study presents the application and a separate chapter introduces the fuzzy Classification Query Language (fCQL) toolkit for implementing these concepts. In its appendix the book lists the fuzzy set operators and the query language’s grammar.

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Nicolas Werro is currently "Head of Business Analytics" in the "Big Data and Business Intelligence" department of Swisscom AG. He achieved his PhD thesis in 2008 at the University of Fribourg in the research group "Information Systems" led by Professor Andreas Meier, where he has been assistant until the end of 2007. Before starting his doctoral studies, Nicolas Werro achieved a diploma degree in Computer Science and Economics as well as a master degree in Computer Science from the University of Fribourg.