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

Ka Ho Leung, Ching Chug Luk, King Lun Tommy Choy, Hoi Yan Lam, Ka Man Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.
Original languageEnglish
Pages (from-to)6528-6551
JournalInternational Journal of Production Research
Volume57
Issue number20
DOIs
Publication statusPublished - Oct 2019

Keywords

  • Data mining
  • Artificial intelligence
  • Market intelligence
  • Fuzzy association rule mining
  • Decision support system
  • E-commerce

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