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A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment

Author

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  • K.H. Leung
  • C.C. Luk
  • K.L. Choy
  • H.Y. Lam
  • Carman K.M. Lee

Abstract

In the era of digitalisation, e-commerce retail sites have become decisive channels for reaching millions of potential customers worldwide. Digital marketing strategies are formulated by the marketing teams in order to increase the traffic on their e-commerce sites, thereby boosting the sales of the products. With the massive amount of data available from the cloud, which were conventionally made with a high degree of intuition based on decision makers’ knowledge and experience, can now be supported with the application of artificial intelligence techniques. This paper introduces a novel approach in applying the fuzzy association rule mining approach and the fuzzy logic technique, for discovering the factors influencing the pricing decision of products launched in e-commerce retail site, and in formulating flexible, dynamic pricing strategies for each product launched in an e-commerce site. A pricing decision support system for B2B e-commerce retail businesses, namely Smart-Quo, is developed and implemented in a Hong Kong-based B2B e-commerce retail company. A six-month pilot run reveals a significant improvement in terms of the efficiency and effectiveness in making pricing decisions on each product. The case study demonstrates the feasibility and potential benefits of applying artificial intelligence techniques in marketing management in today’s digital age.

Suggested Citation

  • K.H. Leung & C.C. Luk & K.L. Choy & H.Y. Lam & Carman K.M. Lee, 2019. "A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment," International Journal of Production Research, Taylor & Francis Journals, vol. 57(20), pages 6528-6551, October.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:20:p:6528-6551
    DOI: 10.1080/00207543.2019.1566674
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    Citations

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    Cited by:

    1. Adrian Micu & Angela-Eliza Micu & Marius Geru & Alexandru Capatina & Mihaela-Carmen Muntean, 2021. "The Impact of Artificial Intelligence Use on the E-Commerce in Romania," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 137-137, February.
    2. Harald Konnerth, 2023. "The Potential Of Ai In B2b E-Commerce: A Structured Literature Review," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 17(1), pages 114-133.
    3. Surajit Bag & Shivam Gupta & Ajay Kumar & Uthayasankar Sivarajah, 2021. "An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance," Post-Print hal-03188195, HAL.
    4. Behera, Rajat Kumar & Bala, Pradip Kumar & Rana, Nripendra P. & Kizgin, Hatice, 2022. "Cognitive computing based ethical principles for improving organisational reputation: A B2B digital marketing perspective," Journal of Business Research, Elsevier, vol. 141(C), pages 685-701.
    5. Norzalita Abd Aziz & Fei Long & Wan Mohd Hirwani Wan Hussain, 2023. "Examining the Effects of Big Data Analytics Capabilities on Firm Performance in the Malaysian Banking Sector," IJFS, MDPI, vol. 11(1), pages 1-13, January.
    6. Lina Guo, 2022. "Cross-border e-commerce platform for commodity automatic pricing model based on deep learning," Electronic Commerce Research, Springer, vol. 22(1), pages 1-20, March.
    7. Cheng-An Tsai & Che-Wei Chang, 2022. "Development of a Partial Shipping Fees Pricing Model to Influence Consumers’ Purchase Intention under the COVID-19 Pandemic," Energies, MDPI, vol. 15(5), pages 1-16, March.

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