NDLI: Objective Cluster Analysis in Value-Based Customer Segmentation Method

Content Provider	IEEE Xplore Digital Library
Author	Hengjun Zhao Changzheng He
Copyright Year	2008
Abstract	Clustering is popular used in customer value segmentation in business research. Compared with other clustering methods, the objective clustering analysis can automatically and objectively determine the number of clusters and find out the optimal clustering scheme. This investigation discussed the reasonable evaluation system of value-driven customer segmentation, identified customer behavior using a recency, frequency and monetary(RFM) index and customer basic properties as integrated variables, and then, presented a novel approach-objective clustering analysis that be used in value-based customer segmentation. The shortcoming of the extra criterion was appraised by its algorithm. The new criterion is followed and be used in segmentation. To demonstrate the efficiency of the proposed method, this work performs an empirical study of a standard datasets of a book club to segment its customers. The experimental results demonstrate that the proposed method can more effectively target clustering groups than the former one.
Starting Page	484
Ending Page	487
File Size	288892
Page Count	4
File Format	PDF
ISBN	9780769535432
DOI	10.1109/WKDD.2009.161
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-23
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	consistency criterion Clustering methods Partitioning algorithms Appraisal Data mining Helium Equations value-driven segmentation Clustering algorithms Frequency Mathematical model Books objective clustering analysis
Content Type	Text
Resource Type	Article

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