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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Detalles Bibliográficos
Autor: Chambi Condori, Pedro Pablo
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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spelling 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
repository.mail.fl_str_mv
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