A deep hybrid learning model for customer repurchase behavior
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Scholar |
Altres documents de l'autoria: Kim, Jina; Ji, honggeun; Oh, Soyoung; Hwang, Syjung; Park, Eunil; del Pobil, Angel P.
Metadades
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comunitat-uji-handle3:10234/8620
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https://doi.org/10.1016/j.jretconser.2020.102381 |
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Títol
A deep hybrid learning model for customer repurchase behaviorData de publicació
2020-11-28Editor
ElsevierISSN
0969-6989Cita bibliogràfica
KIM, Jina, et al. A deep hybrid learning model for customer repurchase behavior. Journal of Retailing and Consumer Services, 2021, vol. 59, p. 102381.Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://www.sciencedirect.com/journal/journal-of-retailing-and-consumer-servicesVersió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
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. [-]
Publicat a
Journal of Retailing and Consumer Services Volume 59, March 2021, 102381Dades relacionades
https://www.sciencedirect.com/science/article/pii/S0969698920313898#tbl1Entitat finançadora
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)
Codi del projecte o subvenció
IITP-2020-0-01816 | NRF-2020R1F1A1048225 | NRF-2020R1C1C1004324
Drets d'accés
© 2020 Elsevier Ltd. All rights reserved.
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
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