Increasing sample size compensates for data problems in segmentation studies
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Survey data frequently serve as the basis for market segmentation studies. Survey data, however, are prone to a range of biases. Little is known about the effects of such biases on the quality of data-driven market segmentation solutions. This study uses artificial data sets of known structure to study the effects of data problems on segment recovery. Some of the data problems under study are partially under the control of market research companies, some are outside their control. Results indicate that (1) insufficient sample sizes lead to suboptimal segmentation solutions; (2) biases in survey data have a strong negative effect on segment recovery; (3) increasing the sample size can compensate for some biases; (4) the effect of sample size increase on segment recovery demonstrates decreasing marginal returns; and—for highly detrimental biases—(5) improvement in segment recovery at high sample size levels occurs only if additional data is free of bias.
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Insufficient sample size and other data-quality issues can influence the validity of segmentation solutions and thereby misguide the practical implications for commercial purposes.
“Insufficient sample size and other data-quality issues can influence the validity of segmentation solutions and thereby misguide the practical implications for commercial purposes (Dolnicar & Grün, 2017;Dolnicar, Grün, & Leisch, 2016).”
From Relationships between functional food consumption and individual traits and values: A segmentation approach (2021) · cites this paper as Dolnicar et al., 2016
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- Authors
- Dolnicar, S., Grün, B., & Leisch, F.
- Affiliations
- Johannes Kepler University of Linz (AT)
- BOKU University (AT)
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 69
- Issue
- 2
- Pages
- 992-999
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W2177427102
- Cited by
- 131
- References
- 72
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 20 · 2023: 17 · 2022: 12 · 2021: 14 · 2020: 14 · 2019: 17 · 2018: 12 · 2017: 6 · 2016: 3
- FWCI
- 17.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Business Research (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- WU Research (submittedVersion)
- Funders
- Australian Research Council (ARC)
- Austrian Science Fund (FWF) (DP110101347, LX0881890, V170-N18)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Keywords
- market segmentation, sample size, data quality, artificial datasets, survey sampling, data-driven segmentation
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