Multi-Factor Ablation Study in Dynamic Pricing for Electronic Products: Comparing Deep Reinforcement Learning with Traditional Optimization

The dynamic pricing of electronic products is of great significance in the market economy, particularly for revenue optimization in e-commerce. The traditional pricing methods show obvious limitations in the face of the rapidly changing market environment and fluctuations in consumer behavior. This study constructs a systematic dynamic pricing ablation experimental framework, through the accurate deconstruction of the market complexity and quantifies the contribution of four key market factors (price elasticity, brand competitiveness, category popularity, and market randomness) that affect the effect of merchants pricing strategy. At the same time, At the same time, the research compares the performance of traditional optimization algorithms and reinforcement learning algorithms in different complexity market environments, and uses multiple rounds of repeated experiments, ANOVA significance test and effect quantity analysis to ensure statistical credibility, providing a reproducible experimental paradigm and empirical basis for dynamic pricing related research. The research realizes market factor decoupling and verifies the natural advantages of the reinforcement learning algorithm in the face of a highly complex market environment; at the same time, it explains the key role of price elasticity in commodity pricing and provides merchants with scientific pricing and investment strategies for algorithm-environment matching.