Editing Market segmentation (section)

=== Statistical techniques used in segmentation ===
[[File:Clustering.jpg|thumb|300px|Visualisation of market segments formed using clustering methods]]

Marketers often engage commercial research firms or consultancies to carry out segmentation analysis, especially if they lack the statistical skills to undertake the analysis. Some segmentation, especially post-hoc analysis, relies on sophisticated statistical analysis.

Common statistical approaches and techniques used in segmentation analysis include:

* Clustering algorithms<ref>https://inseaddataanalytics.github.io/INSEADAnalytics/CourseSessions/Sessions45/ClusterAnalysisReading.html., ''Cluster Analysis and Segmentation'', Online: inseaddataanalytics.github.io/INSEADAnalytics/Report_s45.html [with worked example]</ref> – overlapping, non-overlapping and fuzzy methods; e.g. [[K-means]] or other [[Cluster analysis]]
* [[Conjoint analysis]]<ref>Desarbo, W.S., Ramaswamy, V. and Cohen, S. H., "Market segmentation with choice-based conjoint analysis," ''Marketing Letters,'' vol. 6, no. 2 pp. 137–147.</ref>
* Ensemble approaches – such as [[random forest]]s<ref>Perbert, F., Stenger, B. and Maki, A., "Random Forest Clustering and Application to Video Segmentation," [Research Paper], Toshiba Europe, 2009, Online: https://mi.eng.cam.ac.uk/~bdrs2/papers/perbet_bmvc09.pdf</ref>
* [[Chi-square automatic interaction detection]] – a type of decision-tree<ref>Dell Software, ''Statistics Textbook'', Online: https://documents.software.dell.com/statistics/textbook/customer-segmentation {{Webarchive|url= https://web.archive.org/web/20161022161158/https://documents.software.dell.com/statistics/textbook/customer-segmentation |date=2016-10-22 }}</ref>
* [[Factor analysis]] or [[principal components analysis]]<ref>Minhas, R.S. and Jacobs, E.M., "Benefit Segmentation by Factor Analysis: An improved method of targeting customers for financial services", ''International Journal of Bank Marketing,'' Vol. 14, no. 3, pp. 3–13.</ref>
* [[Latent class model|Latent Class Analysis]] – a generic term for a class of methods that attempt to detect underlying clusters based on observed patterns of association<ref>Wedel, M., and Kamakura, W.A., ''Market Segmentation: Conceptual and Methodological Foundations,'' Springer Science & Business Media, 2010, p. 21.</ref>
* [[Logistic regression]]<ref>Burinskiene, M. and Rudzkiene, V., "Application of Logit Regression Models for the Identification of Market Segments", ''Journal of Business Economics and Management'', vol. 8, no. 4, 2008, pp. 253–258.</ref>
* [[Multidimensional scaling]] and [[canonical analysis]]<ref>T.P. Beane and D.M. Ennis, "Market Segmentation: A Review", ''European Journal of Marketing'', Vol. 21 no. 5, pp. 20–42.</ref>
* [[Mixture model]]s – e.g., EM estimation algorithm, finite-mixture models<ref>Green, P.E., Carmone, F.J. and Wachspress, D.P., ''Consumer Segmentation Via Latent Class Analysis, ''Journal of Consumer Research, December, 1976, pp. 170–174, DOI: https://dx.doi.org/10.1086/208664</ref>
* Model-based segmentation using simultaneous and [[structural equation modeling]]<ref>Swait, J., "A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data," ''Journal of Retailing and Consumer Services,'' Vol. 1, no. 2, 1994, pp. 77–89.</ref> e.g. [[LISREL]]
* Other algorithms such as [[artificial neural network]]s.<ref>Kelly E Fish, K.E., Barnes, J.H. and Aiken, M.W., "Artificial neural networks: A new methodology for industrial market segmentation," '' Industrial Marketing Management,'' Vol. 24, no. 5, 1995, pp. 431–438.</ref>