This Title All WIREs
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 7.250

Applying data mining on customer relationship management system for leisure coffee‐shop industry: a case study in Taiwan

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract 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

Research logic (solid lines). Data source: this research.

[ Normal View | Magnified View ]

FMDT markets of 85‐Degrees‐C leisure coffee‐shop chain. Data source: this research.

[ Normal View | Magnified View ]

Browse by Topic

Application Areas > Business and Industry

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts