Market segmentation: Machine Learning in Marketing in the Context of COVID-19
Descripción del Articulo
The COVID-19 health crisis has led to unprecedented changes in consumer behavior, as consumers now purchase differently and use different means. Consumers are checking and judging products via electronic devices, shaping trends in consumer segments. This research study aimed to use the clustering mo...
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Formato: | artículo |
Fecha de Publicación: | 2023 |
Institución: | Universidad Nacional Mayor de San Marcos |
Repositorio: | Revistas - Universidad Nacional Mayor de San Marcos |
Lenguaje: | español inglés |
OAI Identifier: | oai:ojs.csi.unmsm:article/23623 |
Enlace del recurso: | https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623 |
Nivel de acceso: | acceso abierto |
Materia: | market research segmentation artificial intelligence COVID-19 investigación de mercados segmentación inteligencia artificial covid-19 |
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Market segmentation: Machine Learning in Marketing in the Context of COVID-19Segmentación de mercado: Machine Learning en marketing en contextos de covid-19Chambi Condori, Pedro PabloChambi Condori, Pedro Pablomarket researchsegmentationartificial intelligenceCOVID-19investigación de mercadossegmentacióninteligencia artificialcovid-19The COVID-19 health crisis has led to unprecedented changes in consumer behavior, as consumers now purchase differently and use different means. Consumers are checking and judging products via electronic devices, shaping trends in consumer segments. This research study aimed to use the clustering model with Machine Learning resources in the analysis of clusters as a resource for consumer segmentation, a major component in business marketing management. A 6-question questionnaire was administered to 506 people ranging from 18 to 65 years old to gauge their opinions about going shopping. A dataset was organized using the data collected and processed using RapidMiner Studio 9.10 software. The optimal number of clusters and their components were obtained from the performance indicator provided by Machine Learning.La crisis sanitaria covid-19 ha provocado cambios jamás vistos en el comportamiento de los consumidores, quienes compran de manera diferente y por medios también diferentes. Los consumidores están mirando y valorando los productos a través de dispositivos electrónicos, configurando movimientos en segmentos de consumidores. El objetivo del presente estudio fue aplicar el modelo de clustering con recursos de Machine Learning en el análisis de conglomerados como recurso para la segmentación de consumidores, como un componente importante para la gestión del marketing empresarial. Para dicho propósito, se suministró un cuestionario de 6 preguntas a 506 personas de entre 18 y 65 años para recoger sus percepciones sobre el hecho de salir a comprar. Con los datos recogidos se organizó una dataset para procesarlo en el software RapidMiner Studio 9.10. Como resultado, se obtuvo la cantidad óptima de conglomerados y sus componentes a partir del indicador de performance procurado por Machine Learning.Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos2023-10-18info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdftext/htmltext/htmlaudio/mpegaudio/mpeghttps://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/2362310.15381/idata.v26i1.23623Industrial Data; Vol. 26 No. 1 (2023); 275-301Industrial Data; Vol. 26 Núm. 1 (2023); 275-3011810-99931560-9146reponame:Revistas - Universidad Nacional Mayor de San Marcosinstname:Universidad Nacional Mayor de San Marcosinstacron:UNMSMspaenghttps://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20103https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20104https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20105https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20106https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20107https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20108Derechos de autor 2023 Pedro Pablo Chambi Condorihttps://creativecommons.org/licenses/by/4.0/deed.es_ESinfo:eu-repo/semantics/openAccessoai:ojs.csi.unmsm:article/236232023-10-18T10:05:53Z |
dc.title.none.fl_str_mv |
Market segmentation: Machine Learning in Marketing in the Context of COVID-19 Segmentación de mercado: Machine Learning en marketing en contextos de covid-19 |
title |
Market segmentation: Machine Learning in Marketing in the Context of COVID-19 |
spellingShingle |
Market segmentation: Machine Learning in Marketing in the Context of COVID-19 Chambi Condori, Pedro Pablo market research segmentation artificial intelligence COVID-19 investigación de mercados segmentación inteligencia artificial covid-19 |
title_short |
Market segmentation: Machine Learning in Marketing in the Context of COVID-19 |
title_full |
Market segmentation: Machine Learning in Marketing in the Context of COVID-19 |
title_fullStr |
Market segmentation: Machine Learning in Marketing in the Context of COVID-19 |
title_full_unstemmed |
Market segmentation: Machine Learning in Marketing in the Context of COVID-19 |
title_sort |
Market segmentation: Machine Learning in Marketing in the Context of COVID-19 |
dc.creator.none.fl_str_mv |
Chambi Condori, Pedro Pablo Chambi Condori, Pedro Pablo |
author |
Chambi Condori, Pedro Pablo |
author_facet |
Chambi Condori, Pedro Pablo |
author_role |
author |
dc.subject.none.fl_str_mv |
market research segmentation artificial intelligence COVID-19 investigación de mercados segmentación inteligencia artificial covid-19 |
topic |
market research segmentation artificial intelligence COVID-19 investigación de mercados segmentación inteligencia artificial covid-19 |
description |
The COVID-19 health crisis has led to unprecedented changes in consumer behavior, as consumers now purchase differently and use different means. Consumers are checking and judging products via electronic devices, shaping trends in consumer segments. This research study aimed to use the clustering model with Machine Learning resources in the analysis of clusters as a resource for consumer segmentation, a major component in business marketing management. A 6-question questionnaire was administered to 506 people ranging from 18 to 65 years old to gauge their opinions about going shopping. A dataset was organized using the data collected and processed using RapidMiner Studio 9.10 software. The optimal number of clusters and their components were obtained from the performance indicator provided by Machine Learning. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-18 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623 10.15381/idata.v26i1.23623 |
url |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623 |
identifier_str_mv |
10.15381/idata.v26i1.23623 |
dc.language.none.fl_str_mv |
spa eng |
language |
spa eng |
dc.relation.none.fl_str_mv |
https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20103 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20104 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20105 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20106 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20107 https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/23623/20108 |
dc.rights.none.fl_str_mv |
Derechos de autor 2023 Pedro Pablo Chambi Condori https://creativecommons.org/licenses/by/4.0/deed.es_ES info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Derechos de autor 2023 Pedro Pablo Chambi Condori https://creativecommons.org/licenses/by/4.0/deed.es_ES |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf application/pdf text/html text/html audio/mpeg audio/mpeg |
dc.publisher.none.fl_str_mv |
Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos |
publisher.none.fl_str_mv |
Facultad de Ingeniería Industrial, Universidad Nacional Mayor de San Marcos |
dc.source.none.fl_str_mv |
Industrial Data; Vol. 26 No. 1 (2023); 275-301 Industrial Data; Vol. 26 Núm. 1 (2023); 275-301 1810-9993 1560-9146 reponame:Revistas - Universidad Nacional Mayor de San Marcos instname:Universidad Nacional Mayor de San Marcos instacron:UNMSM |
instname_str |
Universidad Nacional Mayor de San Marcos |
instacron_str |
UNMSM |
institution |
UNMSM |
reponame_str |
Revistas - Universidad Nacional Mayor de San Marcos |
collection |
Revistas - Universidad Nacional Mayor de San Marcos |
repository.name.fl_str_mv |
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repository.mail.fl_str_mv |
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13.887938 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).