Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/28224
Author(s): | Fonseca, J. R. S. Cardoso, M. G. M. S. |
Editor: | Bento, C., Cardoso, A., and Dias, G. |
Date: | 2005 |
Title: | Retail clients latent segments |
Volume: | 3808 |
Book title/volume: | Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science |
Pages: | 348 - 358 |
Event title: | 12th Portuguese Conference on Artificial Intelligence, EPIA 2005 |
Reference: | Fonseca, J. R. S., Cardoso, M. G. M. S. (2005). Retail clients latent segments. In C. Bento, A. Cardoso, & G. Dias (Eds.), Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science (vol. 3808, pp. 348-358). Springer. https://doi.org/10.1007/11595014_35 |
ISSN: | 0302-9743 |
ISBN: | 978-3-540-31646-6 |
DOI (Digital Object Identifier): | 10.1007/11595014_35 |
Keywords: | Clustering Finite mixture models Information criteria Marketing research |
Abstract: | Latent Segments Models (LSM) are commonly used as an approach for market segmentation. When using LSM, several criteria are available to determine the number of segments. However, it is not established which criteria are more adequate when dealing with a specific application. Since most market segmentation problems involve the simultaneous use of categorical and continuous base variables, it is particularly useful to select the best criteria when dealing with LSM with mixed type base variables. We first present an empirical test, which provides the ranking of several information criteria for model selection based on ten mixed data sets. As a result, the ICL-BIC, BIC, CAIC and L criteria are selected as the best performing criteria in the estimation of mixed mixture models. We then present an application concerning a retail chain clients' segmentation. The best information criteria yield two segments: Preferential Clients and Occasional Clients. |
Peerreviewed: | no |
Access type: | Open Access |
Appears in Collections: | BRU-CRN - Comunicações a conferências nacionais |
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