AI-Driven Price Sensitivity Analysis and Consumer Value Optimization for Competitive Markets
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I4P105Keywords:
Price Sensitivity Analysis, Consumer Value Optimization, Competitive Market Strategy, Artificial Intelligence (AI) in Pricing, Machine Learning for Pricing Models, Dynamic Pricing Optimization, Consumer Behavior Analytics, Predictive Demand Modeling, Price Elasticity Estimation, Market Competitiveness Analysis, Data-Driven Pricing StrategiesAbstract
The competitive markets are getting more and more sensitive to competitive pricing strategies, demand; more specific, and less responsive, this is due to more knowledgeable and price sensitive consumers being sensitive to and reactive to unstable external conditions that change in rapid time. Conventional techniques, including both calculus-based elasticity of stature, rule-based decision-making engines, and manual pathfinding are inadequate to characterize nonlinear behaviour, many-facet decision-making patterns and actual-time market oscillations. In order to overcome these shortcomings, this paper proposes AI-powered framework that unites machine learning, causal inference, deep learning and reinforcement learning to create price sensitivity estimates that are very accurate and constantly re-evaluated. The suggested methodology exploits the better feature engineering, demand elasticity modeling, consumer segmentation, and utility-based analysis in order to measure willingness-to-pay and the feature that mostly influences consumer value perceptions formation. It is based on these predictive signals that a multi-objective optimization layer, enabled by reinforcement learning and Bayesian optimization, makes recommendations on the optimal price points that maximize revenues, boost conversions, and positioning. A lot of experiments done with actual retail and e-commerce data reveal a large performance boost up to 23-41 in price sensitivity predictive accuracy, 18-32 in uplift of revenue, and 28 in consumer value alignment, relative to the economy and statistical models. The results show that the framework is scalable, decipherable, and adaptable to fluctuate market environments that the firms can operationalize of intelligent, consumer focused pricing strategies. The study contributes to the idea of AI-based pricing analytics and provides an example of a lasting blueprint of implementing strong data-driven pricing systems in competitive and moderately dynamic markets
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