Dynamic Pricing Algorithms in Global Online Retail: A Literature Review

Abstract

This article explores how dynamic pricing algorithms are
reshaping global online retail, focusing on their ability to
optimize prices in real time and adapt to uncertain market
conditions. The central question guiding this review is how
algorithmic approaches influence revenue performance,
consumer behavior, and market competition. Using a
systematic literature review, the study synthesizes research
across operations research, economics, and marketing to
map key themes. The results show that learning-based
models effectively balance exploration and exploitation,
while feature-driven approaches integrate high-dimensional
data for personalized pricing. Discussion highlights both
opportunities and risks: algorithms enhance efficiency and
profitability but may also trigger tacit collusion, instability,
and consumer concerns about fairness and transparency.
The findings suggest that successful implementation
requires balancing technical sophistication with ethical
safeguards, operational integration, and regulatory
oversight. Ultimately, dynamic pricing’s transformative
potential lies in aligning efficiency gains with consumer trust
and market accountability.