Data Science and Product Pricing Strategy: A Comprehensive Review of Dynamic Pricing, Elasticity Estimation, and Intelligent Pricing Models

Pricing strategy has evolved from static markup models to sophisticated data-driven approaches that leverage machine learning, real-time analytics, and behavioral economics. This comprehensive review examines the intersection of data science and pricing strategy, exploring dynamic pricing optimization, price elasticity estimation, agentic pricing models, customer segmentation, value-based pricing, predictive analytics, Key Value Item management, long tail pricing, outcome-based models, tariff and supply chain considerations, and real-time competitive intelligence. We synthesize current methodologies, review practical applications across industries, and discuss the technical frameworks that enable modern pricing systems. This paper serves as a foundational reference for researchers and practitioners seeking to understand how data science transforms pricing from an art into a rigorous, quantitative discipline.