Dynamic Price Model for E-Commerce

Year : 2026 | Volume : 13 | Issue : 02 | Page : 45 55
By

Saurabh Shah,

Bhavna Sharma,

Kamlesh Lakhwani,

  1. Student, Department of CSE, JECRC University, Rajasthan, India
  2. Associate Professor, Department of CSE, JECRC University, Rajasthan, India
  3. Associate Professor, Department of CSE, JECRC University, Rajasthan, India

Abstract

Retailers may now respond to fluctuating demand, inventory levels, competitor activities, consumer traits, and market circumstances using dynamic pricing, a data-driven technique that has become more significant in e-commerce. Modern pricing systems are much more complex and responsive than they were in the past due to the fast advancements in artificial intelligence (AI), machine learning (ML), big data analytics, and reinforcement learning. This review critically examines recent developments in dynamic pricing for e-commerce, with particular emphasis on algorithmic pricing, ML models, personalized pricing, competitive pricing, and reinforcement learning approaches. A structured literature review approach was adopted to identify relevant empirical and primary research from major scholarly databases, with greater emphasis on recent studies representing the current state-of-the-art. The reviewed evidence indicates that advanced pricing algorithms can improve demand prediction, automate price adjustments, strengthen inventory utilization, increase revenue opportunities, and enhance responsiveness to rapidly changing competitive environments. Recent empirical studies further demonstrate the growing effectiveness of ML and online-learning approaches in real-world pricing applications. Nevertheless, the analysis identifies important challenges concerning consumer fairness, price discrimination, privacy, algorithmic transparency, data quality, model explainability, and the possibility of algorithmically facilitated anticompetitive outcomes. The review also reveals that existing studies frequently concentrate on isolated algorithms, platforms, or short observation periods, leaving limited evidence regarding long-term performance, cross-platform generalizability, consumer responses, and responsible AI-based pricing. The study concludes that future dynamic pricing systems should integrate predictive accuracy and profitability with explainability, fairness constraints, privacy protection, and continuous evaluation of consumer and competitive outcomes.

Keywords: Dynamic pricing, e-commerce, machine learning, algorithmic pricing, big data analytics, consumer behavior, price optimization, fairness and ethics

[This article belongs to E-Commerce for Future & Trends ]

How to cite this article: Saurabh Shah, Bhavna Sharma, Kamlesh Lakhwani. Dynamic Price Model for E-Commerce. E-Commerce for Future & Trends. 2026; 13(02):45-55.
How to cite this URL: Saurabh Shah, Bhavna Sharma, Kamlesh Lakhwani. Dynamic Price Model for E-Commerce. E-Commerce for Future & Trends. 2026; 13(02):45-55. Available from: https://journals.stmjournals.com/ecft/article=2026/view=259376

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Regular Issue Subscription Original Research
Volume 13
Issue 02
Received 11/02/2026
Accepted 31/08/2026
Published 10/09/2026
Publication Time 211 Days

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