AI-driven personalization: Advancements in dynamic pricing and recommendation systems

Nakirikanti, Santosh (2025) AI-driven personalization: Advancements in dynamic pricing and recommendation systems. World Journal of Advanced Research and Reviews, 26 (1). pp. 1778-1791. ISSN 2581-9615

AI-driven personalization has transformed e-commerce from a competitive advantage into an essential business component. This transformation reflects evolving consumer expectations for tailored experiences across all digital touchpoints. Dynamic pricing systems now leverage machine learning algorithms to optimize prices based on demand patterns, inventory levels, competitor actions, and individual shopping behaviors. Meanwhile, recommendation engines have advanced beyond collaborative filtering to incorporate deep neural networks that process unstructured data, identify complex relationships between behaviors and product attributes, and analyze entire customer journeys. The integration of these technologies delivers significant revenue growth, conversion rate improvements, average order value increases, and customer lifetime value extensions. Despite implementation challenges involving data quality, integration complexity, and ethical considerations, emerging directions like federated learning, explainable AI, and cross-channel coherence promise to address current limitations while expanding capabilities.