Share these talks and lectures with your colleagues
Invite colleaguesResearch paper
Comparing clustering methods for market segmentation: A simulation study
Applied Marketing Analytics: The Peer-Reviewed Journal,
2
(3),
225-238
(2016)
Abstract
This paper compares clustering methods on simulated data sets with different characteristics, such as degree of variance, whether there are overlaps between segments, the nature of the true clusters, and the absence/presence of categorical variables. Specifically, the paper compares K-means with latent class and ensemble analysis. The authors’ findings show that latent class analysis performs best in most cases, both in its ability to recover the true cluster members and in its ability to identify the correct number of clusters. Ensemble methods perform second best. K-means performs reasonably well with continuous variables. The current authors also tested the core member approach that can be applied on top of any clustering method, and found that it improved the identification of the correct cluster members.
Keywords: market segmentation; cluster analysis; ensemble analysis; latent class analysis; simulation
The full article is available to subscribers to the journal.
Author's Biography
Chad Vidden has a PhD in Applied Mathematics, with expertise in computational mathematics, data science and machine learning. He is currently an assistant professor at the University of Wisconsin – La Crosse, where he leads a data science and mathematical modelling research group that collaborates with local companies.
Marco Vriens has a PhD in marketing analytics and is a recognised expert in applied analytics. He led analytics teams for Microsoft, GE and supplier firms. Marco is the author of three books: ‘The Insights Advantage: Knowing How to Win’ (2012), ‘Handbook of Marketing Research’ (2006) and ‘Conjoint Analysis in Marketing’ (1995). Marco has been published in academic and industry journals and has won several best paper awards including the David K. Hardin Award.
Song Chen has a PhD in Applied Mathematics and is an assistant professor at University of Wisconsin – La Crosse. He conducts research in scientific computing and data mining with applications in fields such as marketing. He is director of the data science group at University of Wisconsin – La Crosse and actively leads undergraduate collaborative projects with local industry.
Citation
Vidden, Chad, Vriens, Marco and Chen, Song (2016, September 6). Comparing clustering methods for market segmentation: A simulation study. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 2, Issue 3. https://doi.org/10.69554/BUKQ9565.
© Henry Stewart
Publications LLP
Publications LLP