Pricing Optimization across Domains: A Comparative Review
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I3P103Keywords:
Pricing Optimization, Comparative Review, Multi-Domain Pricing, Dynamic Pricing, Machine Learning in Pricing, AI for Pricing Strategies, Revenue Management, Cost-Benefit Analysis, Market Segmentation, Data-Driven Pricing, Cross-Domain Applications, Price Elasticity, Optimization Models, Competitive Pricing, Decision Support SystemsAbstract
Pricing optimization is a critical capability across industries, integrating methods from rule-based heuristics to advanced artificial intelligence. This condensed literature review compares pricing methodologies in four major sectors – Financial Trading, Retail E-commerce, B2B SaaS/Cloud, and Travel and Hospitality – highlighting both common themes and domain-specific nuances. We outline a methodological taxonomy encompassing simple rule-based strategies, econometric demand modeling, operations research techniques from revenue management, machine learning and reinforcement learning (RL) algorithms, and emerging generative AI approaches. Industry sections detail each domain’s pricing objectives (e.g. profit vs. market share), the unique data available (from high-frequency market data to customer usage patterns), prevalent algorithms (from Black–Scholes models to multi-armed bandits), and ethical considerations (fairness, transparency, and regulation). A comparative matrix and cross-domain case studies (such as cloud services adopting yield management concepts) illustrate key performance metrics and challenges side-by-side. We discuss open challenges – including dynamic algorithmic pricing’s potential for consumer harm – and future trends like personalized AI-driven pricing and autonomous pricing agents. The review maintains an academic tone and brevity, citing over 40 sources, to provide a clear, concise yet comprehensive IEEE-style overview of pricing optimization practices and research across these diverse fields
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