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Game theoretic approach of a novel decision policy for customers based on big data

Author

Listed:
  • Shasha Liu

    (Chongqing University)

  • Bingjia Shao

    (Chongqing University)

  • Yuan Gao

    (Xichang Satellite Launch Center
    China Defense Science and Technology Information Center
    Tsinghua University)

  • Su Hu

    (University of Electronic Science and Technology of China)

  • Yi Li

    (The High School Affiliated to Renmin University of China)

  • Weigui Zhou

    (Xichang Satellite Launch Center)

Abstract

In recent days, big data based analysis in hotel industry become popular. Merchants are attracting clients using the accurate analysis of historic data and predicting the behavior of possible clients to perform proper marketing strategy. To study the principle of the game between clients and merchants, in this work, we propose a novel two-stage game theoretic approach of decision policy for clients when choosing the suitable hotel to stay among many candidates, the merchants will provide a non-cooperative game strategy to attract the attention of potential clients. Analysis of the non-cooperative game method based on big data has been given. Simulation results indicate that, by using our proposed novel method, the average price for clients to choose a satisfied hotel is reduced and the successful rate of stay is increased for merchants, which will bring the expected income to a higher level because of the sticky phenomena of users.

Suggested Citation

  • Shasha Liu & Bingjia Shao & Yuan Gao & Su Hu & Yi Li & Weigui Zhou, 2018. "Game theoretic approach of a novel decision policy for customers based on big data," Electronic Commerce Research, Springer, vol. 18(2), pages 225-240, June.
    • Handle: RePEc:spr:elcore:v:18:y:2018:i:2:d:10.1007_s10660-017-9259-6
    DOI: 10.1007/s10660-017-9259-6
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    References listed on IDEAS

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    1. Mike Bennett, 2013. "The financial industry business ontology: Best practice for big data," Journal of Banking Regulation, Palgrave Macmillan, vol. 14(3-4), pages 255-268, July.
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    Cited by:

    1. Hans Weytjens & Enrico Lohmann & Martin Kleinsteuber, 2021. "Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet," Electronic Commerce Research, Springer, vol. 21(2), pages 371-391, June.
    2. Satish Kumar & Weng Marc Lim & Nitesh Pandey & J. Christopher Westland, 2021. "20 years of Electronic Commerce Research," Electronic Commerce Research, Springer, vol. 21(1), pages 1-40, March.

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