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Journal of Statistics and Management Systems

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

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Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

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Journal of Statistics and Management Systems
Open Access Research Article

Generative AI-powered dynamic pricing in e-commerce : A comparative analysis with traditional pricing models

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pp. 51–68Vol. 29Issue 1January 2026DOI: 10.47974/JSMS-1447XML
Received:
16 Oct 2024
Published Online:
17 Jun 2025
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1447
Pages:
51–68

Abstract

Indeed, in e-commerce as an evolving industry, applying generative AI has been regarded as the key to the revolution of the entire pricing strategy. With the increase in the amount of available data and computational resources, the application of dynamic pricing strategy is more characteristic for e-commerce organizations. This article will concentrate on the comparison of the generative AI-based dynamic pricing and the traditional price techniques for instances specifically based on their effects on the efficiencies of revenue management and customer experience. The appearance of the term dynamic pricing in the sphere of e-commerce was possible due to the fact that large amount of data could be analyzed to find such sources of steady and predictable revenue and demand forecast was being made. However, in earlier research on these models, the most used variable was the Price Elasticity of Demand constructed from historical information; however, emerging complexities of consumers and markets required more studies on pricing models incorporating generative AI. This theoretical as well as practical research paper aims to underlie extended knowledge about the generative AI dynamic pricing strategies and assess their efficiency in comparison with the traditional price strategies. Such elements of cost-benefit analysis as cost efficiencies, customer satisfaction levels, opportunities for increasing revenues etc for changing conditions are pinpointed. This research uses literature review and the analysis of cases to examine the key concepts and practical implementation of the generative AI-driven dynamic pricing in e-commerce. It analyses a critical set of factors specifically, personalization strategies, sensitivity to price changes, and perceived value regarding consumer behavior whilst making a purchase in dynamic price context. The study also compares the effectiveness of dynamic pricing systems based on generative AI with traditional models, including fixed, time-based, and competition-based pricing schemes. Further, in this work, it has been illustrated that e-commerce companies can gain significant benefits by integrating generative AI into dynamic pricing frameworks, such as higher revenue, enhanced customer experience.

Keywords

Generative AIDynamic pricingE-commerceRevenue optimizationCustomer behavior

Subject Classifications

68T0768T2068T35

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