Price Sensitivity

Price sensitivity analysis focuses on understanding how changes in price affect consumer behavior and decision-making across various domains, aiming to optimize pricing strategies and resource allocation. Current research emphasizes developing robust and accurate models for predicting price sensitivity, employing techniques like deep learning (including neural networks and transformer models), graph neural networks, and reinforcement learning algorithms to account for complex interactions and data limitations (e.g., censored observations). These advancements have significant implications for diverse fields, including marketing, economics, and operations research, enabling more effective resource management and improved decision-making under uncertainty. The development of cost-sensitive learning methods further enhances the practical applicability of these models in real-world scenarios.