A deep hybrid learning model for customer repurchase behavior
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Other documents of the author: Kim, Jina; Ji, honggeun; Oh, Soyoung; Hwang, Syjung; Park, Eunil; del Pobil, Angel P.
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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https://doi.org/10.1016/j.jretconser.2020.102381 |
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Title
A deep hybrid learning model for customer repurchase behaviorDate
2020-11-28Publisher
ElsevierISSN
0969-6989Bibliographic citation
KIM, Jina, et al. A deep hybrid learning model for customer repurchase behavior. Journal of Retailing and Consumer Services, 2021, vol. 59, p. 102381.Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
Is part of
Journal of Retailing and Consumer Services Volume 59, March 2021, 102381Funder Name
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)
Project code
IITP-2020-0-01816 | NRF-2020R1F1A1048225 | NRF-2020R1C1C1004324
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© 2020 Elsevier Ltd. All rights reserved.
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- ICC_Articles [424]