Using segment level stability to select target segments in data-driven market segmentation studies

Dolnicar, Sara and Leisch, Friedrich (2017) Using segment level stability to select target segments in data-driven market segmentation studies. Marketing Letters, 1-14. doi:10.1007/s11002-017-9423-8

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Author Dolnicar, Sara
Leisch, Friedrich
Title Using segment level stability to select target segments in data-driven market segmentation studies
Journal name Marketing Letters   Check publisher's open access policy
ISSN 0923-0645
1573-059X
Publication date 2017-03-01
Sub-type Article (original research)
DOI 10.1007/s11002-017-9423-8
Open Access Status File (Author Post-print)
Start page 1
End page 14
Total pages 14
Place of publication New York, United States
Publisher Springer
Collection year 2018
Language eng
Abstract Market segmentation is widely used by industry to select the most promising target segment. Most organisations are interested in finding one or a small number of target segments to focus on. Yet, traditional criteria used to select a segmentation solution assess the global quality of the segmentation solution. This approach comes at the risk of selecting a segmentation solution with good overall quality criteria which, however, does not contain groups of consumers representing particularly attractive target segments. The approach we propose helps managers to identify segmentation solutions containing attractive individual segments (e.g., more profitable), irrespective of the quality of the global segmentation solution. We demonstrate the functioning of the newly proposed criteria using two empirical data sets. The new criteria prove to be able to identify segmentation solutions containing individual attractive segments which are not detected using traditional quality criteria for the overall segmentation solution.
Keyword Market segmentation
Niche segments
Cluster analysis
Bootstrap
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published online 1 March 2017

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
UQ Business School Publications
 
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Created: Tue, 14 Mar 2017, 09:33:10 EST by Karen Morgan on behalf of UQ Business School