Required sample sizes for data-driven market segmentation analyses in tourism

Dolnicar, Sara, Grün, Bettina, Leisch, Friedrich and Schmidt, Kathrin (2014) Required sample sizes for data-driven market segmentation analyses in tourism. Journal of Travel Research, 53 3: 296-306. doi:10.1177/0047287513496475

Author Dolnicar, Sara
Grün, Bettina
Leisch, Friedrich
Schmidt, Kathrin
Title Required sample sizes for data-driven market segmentation analyses in tourism
Journal name Journal of Travel Research   Check publisher's open access policy
ISSN 0047-2875
Publication date 2014-04-01
Year available 2013
Sub-type Article (original research)
DOI 10.1177/0047287513496475
Open Access Status DOI
Volume 53
Issue 3
Start page 296
End page 306
Total pages 11
Place of publication Thousand Oaks, CA, United States
Publisher Sage Publications
Language eng
Formatted abstract
Data analysts in industry and academia make heavy use of market segmentation analysis to develop tourism knowledge and select commercially attractive target segments. Within academic research alone, approximately 5% of published articles use market segmentation. However, the validity of data-driven market segmentation analyses depends on having available a sample of adequate size. Moreover, no guidance exists for determining what an adequate sample size is. In the present simulation study using artificial data of known structure, the impact of the difficulty of the segmentation task on the required sample size is analyzed in dependence of the number of variables in the segmentation base. Under all simulated data circumstances, a sample size of 70 times the number of variables proves to be adequate. This finding is of substantial practical importance because it will provide guidance to data analysts in academia and industry who wish to conduct reliable and valid segmentation studies.
Keyword Market segmentation
Cluster analysis
Sample size
Simulation study
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online before print July 28, 2013

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
UQ Business School Publications
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Citation counts: TR Web of Science Citation Count  Cited 22 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 25 times in Scopus Article | Citations
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Created: Sat, 29 Mar 2014, 03:24:56 EST by Karen Morgan on behalf of UQ Business School