Comparative Analysis of Fixed vs Machine Learning Dynamic Pricing Models: A Computational Performance Study

Abstract

The rise of e-commerce and digital offerings has generated a need for ultra-adaptable pricing policies seeking to maximize revenue while optimizing competitive advantage. Traditional fixed pricing schemes are inherently flawed due to a lack of responsiveness to instantaneous fluctuations in the marketplace, inventory levels, as well as demand inelasticity. This study conducts a detailed computational performance study comparing fixed pricing, standard heuristic dynamic pricing (HDP), advanced Machine Learning (ML)-oriented dynamic pricing schemes, with a special focus on a Bi-LSTM network as well as a hybrid scheme based on Wavelet Decomposition (WD). Through simulated high-frequency transactions as well as marketplace data, model evaluation relies on three critical performance metrics: Total Revenue Generated, Pricing Accuracy (measured through Mean Absolute Percentage Error, MAPE), as well as Computational Latency (vital for real-time utiliz ation). The results indicate that while HDP shows marginal improvements over fixed pricing, ML-based schemes, particularly a hybrid WD-Bi-LSTM model, exhibit substantial revenue maximization (up to 18.5% improvement) as well as forecasting accuracy (MAPE up to 2.1%), though at a slight increase in computational latency remains acceptable for real-time deployment for near real-time deployment. This study provides a quantitative foundation for organizations embracing AI-supportive pricing initiatives with emphasis on trade-offs among model sophistication, predictive potency, as well as functionality performance.