Market Segmentation

Intensions Consulting - Market Segmentation Icon.jpg

Opportunities need segmenting. That’s why we use factor analysis and cluster analysis to identify geo-demographic, psychographic, and behavioural target groups.

Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are statistical techniques that are used to uncover relationships among multiple variables and identify a smaller number of underlying latent factors. These tools have been developed for over one hundred years and have become one of the most frequently used statistical methods for assessing validity in psychological testing (Izquierdo et al., 2014). Exploratory factor analysis and confirmatory factor analysis are both frequently used for developing quantitative measures in psychology (Peterson, 2017), adding to academic literature on theories of intelligence, personality, executive functioning, and psychopathology (Flora & Flake, 2017).

 
Intensions Consulting: American consumer technology segments identified through exploratory factor analysis and k-means clustering (learn more)

Intensions Consulting: American consumer technology segments identified through exploratory factor analysis and k-means clustering (learn more)

 

Likewise, principal components analysis (PCA) is a data analysis technique that can be used to extract information from data to uncover a simplified underlying structure of the data set (Abdi & Williams, 2010). Such statistical tools can be used to segment market audiences into distinct groups based on characteristics such as attitudes and spending habits (James et al., 2013).

 
Intensions Consulting: American climate change segments identified through exploratory factor analysis and k-means clustering (learn more)

Intensions Consulting: American climate change segments identified through exploratory factor analysis and k-means clustering (learn more)

 

Finally, cluster analysis is a family of statistical methods that can be used to separate target populations into clusters, such that meaningful subgroups can be identified on the basis of similar characteristics (Bartholomew, 2011). It can play an important role in helping people to analyze and describe hidden group differences, and has played an important role in a wide variety of fields including business research, psychology, and social science (Wu, 2012).

From travel to fashion, health to technology, climate change to loneliness, Intensions Consulting has used market segmentation to define target audiences, uncover market opportunities, and improve business impact.

REFERENCES

- Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459. doi:10.1002/wics.101

- Bartholomew, D. J. (2011). Analysis of multivariate social science data (2nd ed.). Boca Raton, FL: CRC Press. ISBN: 9781584889618

- Flora, D. B., & Flake, J. K. (2017). The purpose and practice of exploratory and confirmatory factor analysis in psychological research: Decisions for scale development and validation. Canadian Journal of Behavioural Science / Revue Canadienne Des Sciences Du Comportement, 49(2), 78-88. doi:10.1037/cbs0000069

- Izquierdo, I., Olea, J., & Abad, F. J. (2014). Exploratory factor analysis in validation studies: Uses and recommendations. Psicothema, 26(3), 395-400. doi:10.7334/psicothema2013.349

- James, G., Witten, D., Hastie, T., & SpringerLink ebooks - Mathematics and Statistics. (2013). An introduction to statistical learning with applications in R. New York: Springer New York. doi:10.1007/978-1-4614-7138-7

- Peterson, C. (2017). Exploratory factor analysis and theory generation in psychology. Review of Philosophy and Psychology, 8(3), 519-540. doi:10.1007/s13164-016-0325

- Wu, J. (2012). Advances in K-means clustering: A data mining thinking. New York, NY: Springer. ISBN: 978-3-642-29807-3

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