Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16420
Title: Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods
Authors: Chan, KY
Kwong, CK 
Hu, BQ
Keywords: Digital camera design
Fuzzy clustering
Fuzzy compression
Ideal point
Market segmentation
Product design
Issue Date: 2012
Publisher: Elsevier
Source: Applied soft computing, 2012, v. 12, no. 4, p. 1371-1378 How to cite?
Journal: Applied soft computing 
Abstract: In product design, various methodologies have been proposed for market segmentation, which group consumers with similar customer requirements into clusters. Central points on market segments are always used as ideal points of customer requirements for product design, which reflects particular competitive strategies to effectively reach all consumers' interests. However, existing methodologies ignore the fuzziness on consumers' customer requirements. In this paper, a new methodology is proposed to perform market segmentation based on consumers' customer requirements, which exist fuzziness. The methodology is an integration of a fuzzy compression technique for multi-dimension reduction and a fuzzy clustering technique. It first compresses the fuzzy data regarding customer requirements from high dimensions into two dimensions. After the fuzzy data is clustered into marketing segments, the centre points of market segments are used as ideal points for new product development. The effectiveness of the proposed methodology in market segmentation and identification of the ideal points for new product design is demonstrated using a case study of new digital camera design.
URI: http://hdl.handle.net/10397/16420
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2011.11.026
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