Optimal pricing strategies and decision‐making systems in e‐commerce using integrated fuzzy multi‐criteria method

Abstract

AbstractE‐commerce online stores are now virtual platforms for connecting with millions of potential clients worldwide in the age of digitization. The marketing teams develop digital marketing tactics to attract traffic to their e‐commerce sites and increase sales volume. With the vast amount of data provided by the cloud, decisions that were previously made with a significant level of intuition based on the knowledge and experience of decision‐makers can now be backed using artificial intelligence algorithms. To identify the variables influencing pricing decisions for products launched on e‐commerce shopping sites and to develop variable pricing techniques for each product found on an e‐commerce site. This paper introduces a novel approach that applies Fuzzy association rule mining (FARM) and Fuzzy TOPSIS MCDM methodology. A B2B e‐commerce marketing store based in Hong Kong has created and implemented Smart‐Quo, a pricing decision support system for B2B e‐commerce retail businesses. After a six‐month trial period, there has been a substantial advance in the effectiveness and efficiency of choosing prices for each product. The case study illustrates the viability and potential advantages of implementing artificial intelligence tools in marketing management in the digital era.