Behind Price Tags: A 3+11 Dynamic Pricing Strategies Review Framework in E-Commerce

The rise of e-commerce has made dynamic pricing an increasingly prominent practice, enabled by reduced menu costs and immediate access to competitive market data. Various pricing strategies have emerged in response to these conditions, yet there is a lack of clear definitions and cohesive technological frameworks for these strategies. This paper provides a structured review of dynamic pricing in the digital marketplace, introducing a 3+11 framework, which categories eleven representative pricing mechanisms into three core strategies: rule- based, algorithmic artificial intelligence-based, and engagement-driven. The paper particularly emphasizes the integration of artificial intelligence (AI) into pricing design, building on the discussion of dynamic pricing strategies. Reinforcement learning (RL) models are central to creating adaptive, data-driven pricing policies. In contrast, large language models (LLMs) provide new opportunities for personalization, negotiation, and understanding consumer behavior semantically. The framework illustrates how AI is reshaping existing pricing techniques and facilitating the creation of novel, interactive pricing formats. This research provides a consolidated view of the field and guides researchers and practitioners seeking to navigate the evolving intersection of pricing strategy and intelligent systems.