z-logo
open-access-imgOpen Access
How to Avoid Random Market Segmentation Solutions
Author(s) -
Dominik Ernst,
Sara Dolničar
Publication year - 2017
Publication title -
journal of travel research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.403
H-Index - 132
eISSN - 1552-6763
pISSN - 0047-2875
DOI - 10.1177/0047287516684978
Subject(s) - market segmentation , segmentation , tourism , computer science , market data , exploratory research , econometrics , artificial intelligence , marketing , economics , business , geography , finance , archaeology , sociology , anthropology
Tourism researchers and the tourism industry rely heavily on data-driven market segmentation analysis for both knowledge development and market insight. Most algorithms used in data-driven market segmentation are exploratory; they do not generate one single stable result. Only when data are well-structured (when very clear, distinct market segments exist in the data) are repeated calculations likely to generate the same segmentation solution. When data lack structure, which is frequently the case in empirical consumer data sets, repeated calculations lead to different solutions. Running a market segmentation analysis once only can therefore lead to an entirely random solution that does not represent a strong foundation for developing a long-term market segmentation strategy. The present study (1) explains the problem, (2) assesses how high the risk is of random solutions occurring in tourism market segmentation studies, and (3) recommends an approach that can be used to avoid random solutions.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom