Latent segmentation procedures are usually aimed at detecting the heterogeneity of statisitical units with reference to a system of relationships described by a structural equation model with latent variables (SEM-LV). After a short review of hte main proposals given in the literature, we present a descriptive two-step procedure, wich first considers either an a-priori or a post-hoc segmentation approach: the former is based upon the existence of proper classification variables; the latter defines as classificaiton variables the latent scores estimated by a global SEM-LV model. In the second step a cluster analysis is performed by using the previously defined grouping vairables, in order to improve the classification by means of a cluster-wise Partial Least Squares algorithm; the classification refinement is executed with reference both to the SEM inner and outer models.
Boari, G., Cantaluppi, G., Further Considerations on Latent Segmentation Techniques for Customer Heterogeneity Detection, in Classification and Data Analysis 2009, Book of Short Papers, 7° Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, (Catania, 09-11 September 2009), Cleup, Padova 2009: 255-258 [http://hdl.handle.net/10807/23798]
Further Considerations on Latent Segmentation Techniques for Customer Heterogeneity Detection
Boari, Giuseppe;Cantaluppi, Gabriele
2009
Abstract
Latent segmentation procedures are usually aimed at detecting the heterogeneity of statisitical units with reference to a system of relationships described by a structural equation model with latent variables (SEM-LV). After a short review of hte main proposals given in the literature, we present a descriptive two-step procedure, wich first considers either an a-priori or a post-hoc segmentation approach: the former is based upon the existence of proper classification variables; the latter defines as classificaiton variables the latent scores estimated by a global SEM-LV model. In the second step a cluster analysis is performed by using the previously defined grouping vairables, in order to improve the classification by means of a cluster-wise Partial Least Squares algorithm; the classification refinement is executed with reference both to the SEM inner and outer models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.