Skip to main content

Methods in Segmentation

  • Chapter
  • First Online:

Abstract

Irrespective of the method used, market segmentation analysis is exploratory in nature. This means that any analysis, with any kind of data, will lead to a result, and different competing solutions might emerge where no clear best solution is discernible. It is critical, therefore, to be aware of all potential methodological pitfalls. This chapter discusses all steps required for successful application of both common sense and data-driven market segmentation. Critical decisions are highlighted. In particular, in data-driven market segmentation: (1) data should be collected carefully and in view of the intended segmentation analysis; (2) the sample size should be sufficient to accommodate the number of variables in the segmentation base; (3) data structure should be explored to learn about the most appropriate segmentation concept and to select the most suitable number of segments; (4) a suitable algorithm should be chosen; and (5) segments should be profiled in detail to meaningfully inform target segment selection and, ultimately, the development of an effective marketing mix.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 19(1), 64–73.

    Google Scholar 

  • Croft, M. J. (1994). Market segmentation: a step-by-step guide to profitable new business. New York: Routledge.

    Google Scholar 

  • Day, G. S. (1984). Strategic market planning. Minnesota: West Publishing Company.

    Google Scholar 

  • Dempster, A. P., Laird, N. N., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM-algorithm. Journal of the Royal Statistical Society B, 39(1), 1–38.

    Google Scholar 

  • Dibb, S., & Simkin, L. (2008). Market segmentation success: Making it happen! New York: The Haworth Press.

    Google Scholar 

  • Dolnicar, S. (2004). Beyond “commonsense segmentation”—A systematics of segmentation approaches in tourism. Journal of Travel Research, 42(3), 244–250.

    Article  Google Scholar 

  • Dolnicar, S., & Grün, B. (2008). Challenging “factor cluster segmentation”. Journal of Travel Research, 47(1), 63–71.

    Article  Google Scholar 

  • Dolnicar, S., & Grün, B. (2009). Response style contamination of student evaluation data. Journal of Marketing Education, 31(2), 160–172.

    Article  Google Scholar 

  • Dolnicar, S., Grün, B., & Leisch, F. (2016). Increasing sample size compensates for data problems in segmentation studies. Journal of Business Research, 69, 992–999.

    Article  Google Scholar 

  • Dolnicar, S., & Lazarevski, K. (2009). Methodological reasons for the theory/practice divide in market segmentation. Journal of Marketing Management, 25(3/4), 357–374.

    Article  Google Scholar 

  • Dolnicar, S., & Leisch, F. (2010). Evaluation of structure and reproducibility of cluster solutions using the bootstrap. Marketing Letters, 21(1), 83–101.

    Article  Google Scholar 

  • Dolnicar, S., & Leisch, F. (2013). Using graphical statistics to better understand market segmentation solutions. International Journal of Market Research, 56(2), 97–120.

    Google Scholar 

  • Dolnicar, S., Rossiter, J. R., & Grün, B. (2012). “Pick-any” measures contaminate brand image studies. International Journal of Market Research, 54(6), 821–834.

    Article  Google Scholar 

  • Everitt, B., Landau, S., Leese, M., & Stahl, D. (2010). Cluster analysis (5th ed.). New York: Wiley.

    Google Scholar 

  • Fraley, C., & Raftery, A. E. (2002). Model-based clustering discriminant analysis and density estimation. Journal of the American Statistical Association, 97(458), 611–631.

    Article  Google Scholar 

  • Frühwirth-Schnatter, S. (2006). Finite mixture and Markov switching models. Berlin: Springer.

    Google Scholar 

  • Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27(4), 857–871.

    Google Scholar 

  • Hebert, J. R., Clemow, L., Pbert, L., Ockene, I. S., & Ockene, J. K. (1995). Social desirability bias in dietary self-report may compromise the validity of dietary intake measures. International Journal of Epidemiology, 24(2), 389–398.

    Article  Google Scholar 

  • Hui, C. H., & Triandis, H. C. (1989). Effects of culture and response format on extreme response style. Journal of Cross-Cultural Psychology, 20(3), 296–309.

    Article  Google Scholar 

  • Johnson, M. D., Lehmann, D. R., & Horne, D. R. (1990). The effects of fatigue on judgments of interproduct similarity. International Journal of Research in Marketing, 7(1), 35–43.

    Article  Google Scholar 

  • Karlsson, L. (2015). The impact of checklists on organizational target segment selection. PhD Thesis, University of Wollongong.

    Google Scholar 

  • Karlsson, L., & Dolnicar, S. (2016). Does eco certification sell tourism services? Evidence from a quasi-experimental observation study in Iceland. Journal of Sustainable Tourism, 24(5), 694–714.

    Google Scholar 

  • Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: Wiley.

    Google Scholar 

  • Kotler, P., & Keller, K. L. (2012). Marketing management (14th ed.). Essex: Pearson Education.

    Google Scholar 

  • Leisch, F. (2006). A toolbox for K-centroids cluster analysis. Computational Statistics & Data Analysis, 51(2), 526–544.

    Article  Google Scholar 

  • MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, (pp. 281–297). California: University of California Press.

    Google Scholar 

  • Marin, G., Gamba, R. J., & Marin, B. V. (1992). Extreme response style and acquiescence among Hispanics—The role of acculturation and education. Journal of Cross-Cultural Psychology, 23(4), 498–509.

    Article  Google Scholar 

  • Mazanec, J. A. (2000). Market segmentation. In J. Jafari (Ed.), Encyclopaedia of tourism. London: Routledge.

    Google Scholar 

  • McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: Wiley.

    Google Scholar 

  • Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2), 159–179.

    Article  Google Scholar 

  • Myers, J. H., & Tauber, E. (1977). Market structure analysis. Chicago, IL: American Marketing Association.

    Google Scholar 

  • Paulhus, D. L. (1991). Measurement and control of response bias. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of personality and social psychological attitudes (pp. 17–59). San Diego: Academic Press.

    Chapter  Google Scholar 

  • Perreault, W. D., & McCarthy, J. (2002). Basic marketing: A global-management approach (14th ed.). New York: McGraw-Hill Irwin.

    Google Scholar 

  • Sharp, B. (2013). Marketing: Theory, evidence, practice. South Melbourne: Oxford University Press.

    Google Scholar 

  • Solomon, M. R., Hughes, A., Chitty, B., Fripp, G., Marshall, G. W., & Stuart, E. W. (2011). Marketing 2: Real people, real choices. Frenchs Forest: Pearson Education Australia.

    Google Scholar 

  • Vermunt, J. K. (2001). The use of restricted latent class models for defining and testing nonparametric and parametric item response theory models. Applied Psychological Measurement, 25(3), 283–294.

    Article  Google Scholar 

  • Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Boston: Kluwer Academic Publishers.

    Book  Google Scholar 

  • West, D., Ford, J., & Ibrahim, E. (2010). Strategic marketing: Creating competitive advantage (2nd ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Winer, R., & Dhar, R. (2011). Marketing management (4th ed.). London: Pearson.

    Google Scholar 

Download references

Acknowledgments

We thank the Australian Research Council (ARC) for funding support under Project DP110101347.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sara Dolnicar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media Singapore

About this chapter

Cite this chapter

Dolnicar, S., Grün, B. (2017). Methods in Segmentation. In: Dietrich, T., Rundle-Thiele, S., Kubacki, K. (eds) Segmentation in Social Marketing. Springer, Singapore. https://doi.org/10.1007/978-981-10-1835-0_7

Download citation

Publish with us

Policies and ethics