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A deep hybrid learning model for customer repurchase behavior

Author

Listed:
  • Kim, Jina
  • Ji, HongGeun
  • Oh, Soyoung
  • Hwang, Syjung
  • Park, Eunil
  • del Pobil, Angel P.

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 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.

Suggested Citation

  • Kim, Jina & Ji, HongGeun & Oh, Soyoung & Hwang, Syjung & Park, Eunil & del Pobil, Angel P., 2021. "A deep hybrid learning model for customer repurchase behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:joreco:v:59:y:2021:i:c:s0969698920313898
    DOI: 10.1016/j.jretconser.2020.102381
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    References listed on IDEAS

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    Cited by:

    1. Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    2. Li, Yanmin & Zhong, Ziqi & Zhang, Fengrui & Zhao, Xinjie, 2022. "Artificial intelligence-based human–computer interaction technology applied in consumer behavior analysis and experiential education," LSE Research Online Documents on Economics 115047, London School of Economics and Political Science, LSE Library.
    3. Noureddine Boustani & Ali Emrouznejad & Roya Gholami & Ozren Despic & Athina Ioannou, 2024. "Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks," Annals of Operations Research, Springer, vol. 339(1), pages 613-630, August.
    4. Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).

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