A deep hybrid learning model for customer repurchase behavior
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Otros documentos de la autoría: Kim, Jina; Ji, honggeun; Oh, Soyoung; Hwang, Syjung; Park, Eunil; del Pobil, Angel P.
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
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https://doi.org/10.1016/j.jretconser.2020.102381 |
Metadatos
Título
A deep hybrid learning model for customer repurchase behaviorFecha de publicación
2020-11-28Editor
ElsevierISSN
0969-6989Cita bibliográfica
KIM, Jina, et al. A deep hybrid learning model for customer repurchase behavior. Journal of Retailing and Consumer Services, 2021, vol. 59, p. 102381.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/journal/journal-of-retailing-and-consumer-servicesVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Smartphones have become an integral part of our daily lives, which has led to the rapid growth of the smartphone market. As the global smartphone market tends to remain stable, retaining existing customers has become ... [+]
Smartphones have become an integral part of our daily lives, which has led to the rapid growth of the smartphone market. As the global smartphone market tends to remain stable, retaining existing customers has become a challenge for smartphone manufacturers. This study investigates whether a deep hybrid learning approach with various customer-oriented types of data can be useful in exploring customer repurchase behavior of same-brand smartphones. Considering data from more than 74,000 customers, the proposed deep learning approach showed a prediction accuracy higher than 90%. Based on the results of deep hybrid learning models, we aim to provide better understanding on customer behavior, such that it could be used as valuable assets for innovating future marketing strategies. [-]
Publicado en
Journal of Retailing and Consumer Services Volume 59, March 2021, 102381Datos relacionados
https://www.sciencedirect.com/science/article/pii/S0969698920313898#tbl1Entidad financiadora
Ministry of Science and ICT (Korea) | ICT Challenge and Advanced Network of HRD | Institute of Information & Communications Technology Planning & Evaluation | National Research Foundation of Korea (NRF)
Código del proyecto o subvención
IITP-2020-0-01816 | NRF-2020R1F1A1048225 | NRF-2020R1C1C1004324
Derechos de acceso
© 2020 Elsevier Ltd. All rights reserved.
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info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
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