Big Data Analytics for Predicting Consumer Demand and Price Sensitivity in E-Commerce
Keywords:
big data analytics; demand prediction; price sensitivity; e-commerce; data governance; dynamic pricing; algorithmic fairness; socio-technical systemsAbstract
The rapid expansion of e-commerce has transformed consumer markets into large-scale digital environments in which behavioral signals, transaction records, and contextual data are generated continuously. Big data analytics now occupies a central role in predicting consumer demand and estimating price sensitivity, yet the design of such systems involves far more than predictive accuracy. This paper presents a system-level examination of the architectures, governance mechanisms, and infrastructure choices that support demand and price sensitivity modeling in online commerce. It analyzes the structural trade-offs among data integration, model complexity, latency, interpretability, and operational resilience. The discussion covers data acquisition and quality management, the integration of structured and unstructured behavioral data, machine learning pipelines for demand forecasting, and dynamic pricing systems that respond to estimated price sensitivity. Special attention is given to governance and policy concerns, including algorithmic fairness, privacy, consumer manipulation, and institutional accountability. The paper further examines cross-domain insights from supply chain analytics, marketing science, and operations management. It argues that sustainable and robust e-commerce analytics requires an institutional architecture that balances predictive power with fairness, transparency, and long-run consumer trust. The conclusion identifies frontiers for future research, including federated learning, causal inference, and regulatory frameworks for algorithmic pricing. Throughout, the paper emphasizes that big data systems for demand and pricing must be understood as socio-technical infrastructures rather than isolated predictive engines.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
