Projects per year
Abstract
There are numerous ways of segmenting a market based on consumer survey data. We introduce bagged clustering as a new exploratory approach in the field of market segmentation research which offers a few major advantages over both hierarchical and partitioning algorithms, especially when dealing with large binary data sets: In the hierarchical step of the procedure the researcher is enabled to inspect if cluster structure exists in the data and gain insight about the number of clusters to extract. The bagged clustering approach is not limited in terms of sample size, nor dimensionality of the data. More stable clustering results are found than with standard partitioning methods (the comparative evaluation is demonstrated for the K-means and the LVQ algorithm). Finally, segment profiles for binary data can be depicted in a more informative way by visualizing bootstrap replications with box plot diagrams. The target audience for this paper thus consists of both academics and practitioners interested in explorative partitioning techniques.
Original language | English |
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Place of Publication | Vienna |
Publisher | SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business |
DOIs | |
Publication status | Published - 2000 |
Publication series
Series | Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science" |
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Number | 71 |
WU Working Paper Series
- Working Papers SFB \Adaptive Information Systems and Modelling in Economics and Management Science\
Projects
- 1 Finished
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Adaptive Modelling - Market segmentation and product positioning
Mazanec, J. (PI - Project head), Buchta, C. (Researcher), Dolnicar, S. (Researcher) & Reutterer, T. (Researcher)
1/01/97 → 31/03/03
Project: Other