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

Vijaya Bhaskar. Reddypogu & U. Devi Prasad

Journal of Statistics and Management Systems2026https://doi.org/10.47974/jsms-1447article
ABDC C
Weight
0.50

What the paper says

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 costbenefit 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.

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https://doi.org/https://doi.org/10.47974/jsms-1447

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@article{vijaya2026,
  title        = {{© Generative AI-powered dynamic pricing in e-commerce : A comparative analysis with traditional pricing models}},
  author       = {Vijaya Bhaskar. Reddypogu & U. Devi Prasad},
  journal      = {Journal of Statistics and Management Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.47974/jsms-1447},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.