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Customer service robot model based on e-commerce dual-channel channel supply coordination and compensation strategy in the perspective of big data

Author

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  • Changhao Zhang

    (Henan University of Animal Husbandry and Economy)

  • Mengyu Ren

    (Henan University of Animal Husbandry and Economy)

Abstract

In order to improve the customer service effect of e-commerce customer service robots, this paper combines the compensation strategy of dual-channel supply chain coordination of e-commerce with the big data perspective to complete the function and system analysis of e-commerce customer service robots. In the study of the dual-channel supply chain, this paper considers the difference between online channels and traditional channel service experience, and uses the dual-channel service competition model to improve the performance of customer service robots. Aiming at the characteristics of online channel service experience and service model, this paper analyzes the model of a single online channel and the dual-channel supply chain model composed of online direct sales channels and traditional single retailer channels. In addition, this paper constructs the customer service robot system structure and uses experiments to verify its performance. The research results show that the algorithm system proposed in this paper is reliable.

Suggested Citation

  • Changhao Zhang & Mengyu Ren, 2023. "Customer service robot model based on e-commerce dual-channel channel supply coordination and compensation strategy in the perspective of big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 591-601, April.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:2:d:10.1007_s13198-021-01325-2
    DOI: 10.1007/s13198-021-01325-2
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    References listed on IDEAS

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    1. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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