Social Media Word-of-Mouth and Dynamic Pricing Strategies in Digital Retail Platforms

Abstract

Digital retail platforms increasingly operate as complex socio-technical infrastructures in which consumer-generated social media word-of-mouth and algorithmic pricing systems interact in real time. This paper develops a systems-level analysis of the integration of word-of-mouth signals into dynamic pricing strategies. It examines the architectural foundations that connect social listening, sentiment analysis, demand forecasting, and revenue management modules, highlighting structural trade-offs between responsiveness, accuracy, latency, and interpretability. The paper further analyzes the data infrastructure required to transform unstructured social discourse into pricing-relevant demand signals, including issues of data provenance, representativeness, bias, and decay. A central concern is the governance of algorithmic pricing systems that respond to social influence, because such systems can amplify demand shocks, encode discriminatory outcomes, facilitate implicit coordination, or destabilize consumer trust. The discussion addresses fairness, transparency, regulatory compliance, and organizational deployment challenges. Drawing on cross-domain insights from marketing science, revenue management, machine learning, and technology policy, the paper argues that sustainable dynamic pricing architectures must balance short-term revenue performance against long-term platform legitimacy, robustness, and equitable treatment of consumers. It concludes by outlining directions for future research on resilient, auditable, and socially accountable pricing infrastructures.