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Applying data mining on customer relationship management system for leisure coffee‐shop industry: a case study in Taiwan
Author(s) -
Chiang WenYu
Publication year - 2013
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1084
Subject(s) - customer lifetime value , business , marketing , customer value , value (mathematics) , customer relationship management , market segmentation , association rule learning , process (computing) , fuzzy logic , computer science , operations research , data mining , customer advocacy , artificial intelligence , economics , engineering , microeconomics , machine learning , profit (economics) , service quality , service (business) , operating system
The objective of this research is to identify high‐value markets by using the data mining technologies and a new model. The well‐known Fuzzy C‐Means algorithm is applied to process the market segmentation of the customer benefit market; and a new model [based on ‘Recency–Frequency–Monetary’ (RFM) model] is applied to process customer value markets for leisure coffee‐shop industry. The results show the relationships between the two types of markets (benefit and customer value), which are presented by fuzzy and nonfuzzy association rules. These rules can be applied to customer relationship management systems for obtaining useful and high‐value markets. The results can help leisure coffee‐shop industry to acquire knowledge of customers, and to identify the explicit customer values for marketing plans. © 2013 Wiley Periodicals, Inc. This article is categorized under: Application Areas > Business and Industry