Autor(es): Brochado, Ana Margarida Mendes Camelo Oliveira
Data: 2009
Identificador Persistente: http://hdl.handle.net/10216/7480
Origem: Repositório Aberto da Universidade do Porto
Assunto(s): Segmentação de mercado; modelos mistura de regressão; critérios de informação; critérios de classificação; design experimental; simulação; Market segmentation; information criteria; classification criteria; experimental design; simulation; CIÊNCIAS EMPRESARIAIS; Porto
This dissertation is developed in the context of market segmentation and market segments retention when mixture regression models for normal data are used. Despite the popularity of these models, the decision of how many segments to keep is an open issue, both in Marketing and Statistics literature. To determine the correct number of market segments is essential, because many strategic Marketing decisions in heterogeneous markets depend on it and an incorrect selection of the number of market segments results in monetary costs for any company. This dissertation has two main objectives: (i) identification, description and classification of the criteria that could be used to select the number of market segments (ii) evaluation of how the reviewed criteria perform and the influence of a set of experimental conditions on the selection of the number of market segments. The reviewed criteria were classified into two groups, namely information criteria (Kullback-Leibler, bayesian and consistent) and classification criteria (probabilistic and fuzzy indices). The performance evaluation of the 26 selected criteria was accomplished through a set of 17 experimental designs. In these experimental designs we considered the problem of market niches and the robustness of the results to the probability assumed (normal versus uniform). The simulations were implemented with Gauss 6.0 package. The best results were obtained for the criteria AIC3, AIC4, HQ, ICLBIC and ICOMPLBIC. BIC and CAIC also perform well with large samples and a large number of market segments.
