Extracting clusters from aggregate panel data: a market segmentation study
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/13138 |
Resumo: | This paper introduces a new application of the Sequential Quadratic Programing (SQP) algorithm to the context of clustering aggregate panel data. The optimization applies the SQP method in parameter estimation. The method is illustrated on synthetic and empirical data sets. Distinct models are estimated and compared with varying numbers of clusters, explanatory variables, and data aggregation. Results show a good performance of the SQP algorithm for synthetic and empirical data sets. Synthetic data sets were simulated assuming two segments and two covariates, and the correlation between the two covariates was controlled in three scenarios: rho = 0.00 (no correlation), rho = 0.25 (weak correlation), and rho = 0.50 (moderate correlation). The SQP algorithm identifies the correct number of segments for these three scenarios based on all information criteria (AIC, AIC3, and BIC) and retrieves the unobserved heterogeneity in preferences. The empirical case study applies the SQP algorithm to consumer purchase data to find market segments. Results for the empirical data set can provide insights for retail category managers because they are able to compute the impact on the marginal shares caused by a change in the average price of one brand or product. |
id |
RCAP_b24c62299c9f555107ffa9f5eab1e726 |
---|---|
oai_identifier_str |
oai:repositorio.iscte-iul.pt:10071/13138 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Extracting clusters from aggregate panel data: a market segmentation studyCluster analysisMarket segmentationPanel dataSequential quadratic programingThis paper introduces a new application of the Sequential Quadratic Programing (SQP) algorithm to the context of clustering aggregate panel data. The optimization applies the SQP method in parameter estimation. The method is illustrated on synthetic and empirical data sets. Distinct models are estimated and compared with varying numbers of clusters, explanatory variables, and data aggregation. Results show a good performance of the SQP algorithm for synthetic and empirical data sets. Synthetic data sets were simulated assuming two segments and two covariates, and the correlation between the two covariates was controlled in three scenarios: rho = 0.00 (no correlation), rho = 0.25 (weak correlation), and rho = 0.50 (moderate correlation). The SQP algorithm identifies the correct number of segments for these three scenarios based on all information criteria (AIC, AIC3, and BIC) and retrieves the unobserved heterogeneity in preferences. The empirical case study applies the SQP algorithm to consumer purchase data to find market segments. Results for the empirical data set can provide insights for retail category managers because they are able to compute the impact on the marginal shares caused by a change in the average price of one brand or product.Elsevier2017-04-26T10:51:28Z2017-01-01T00:00:00Z20172019-03-22T11:03:56Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/13138eng0096-300310.1016/j.amc.2016.10.012Trindade, G.Dias, J. G.Ambrósio, J.info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:42:18Zoai:repositorio.iscte-iul.pt:10071/13138Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:19:46.162127Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Extracting clusters from aggregate panel data: a market segmentation study |
title |
Extracting clusters from aggregate panel data: a market segmentation study |
spellingShingle |
Extracting clusters from aggregate panel data: a market segmentation study Trindade, G. Cluster analysis Market segmentation Panel data Sequential quadratic programing |
title_short |
Extracting clusters from aggregate panel data: a market segmentation study |
title_full |
Extracting clusters from aggregate panel data: a market segmentation study |
title_fullStr |
Extracting clusters from aggregate panel data: a market segmentation study |
title_full_unstemmed |
Extracting clusters from aggregate panel data: a market segmentation study |
title_sort |
Extracting clusters from aggregate panel data: a market segmentation study |
author |
Trindade, G. |
author_facet |
Trindade, G. Dias, J. G. Ambrósio, J. |
author_role |
author |
author2 |
Dias, J. G. Ambrósio, J. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Trindade, G. Dias, J. G. Ambrósio, J. |
dc.subject.por.fl_str_mv |
Cluster analysis Market segmentation Panel data Sequential quadratic programing |
topic |
Cluster analysis Market segmentation Panel data Sequential quadratic programing |
description |
This paper introduces a new application of the Sequential Quadratic Programing (SQP) algorithm to the context of clustering aggregate panel data. The optimization applies the SQP method in parameter estimation. The method is illustrated on synthetic and empirical data sets. Distinct models are estimated and compared with varying numbers of clusters, explanatory variables, and data aggregation. Results show a good performance of the SQP algorithm for synthetic and empirical data sets. Synthetic data sets were simulated assuming two segments and two covariates, and the correlation between the two covariates was controlled in three scenarios: rho = 0.00 (no correlation), rho = 0.25 (weak correlation), and rho = 0.50 (moderate correlation). The SQP algorithm identifies the correct number of segments for these three scenarios based on all information criteria (AIC, AIC3, and BIC) and retrieves the unobserved heterogeneity in preferences. The empirical case study applies the SQP algorithm to consumer purchase data to find market segments. Results for the empirical data set can provide insights for retail category managers because they are able to compute the impact on the marginal shares caused by a change in the average price of one brand or product. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-04-26T10:51:28Z 2017-01-01T00:00:00Z 2017 2019-03-22T11:03:56Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/13138 |
url |
http://hdl.handle.net/10071/13138 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0096-3003 10.1016/j.amc.2016.10.012 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799134757690277888 |