Optimizing Omnichannel Dynamic Pricing Strategy Through Empirical Price Elasticity Modeling

In hyper-competitive omnichannel retail environments, optimizing dynamic pricing strategies across interconnected sales channels (e-commerce platforms, brick-and-mortar stores, mobile apps, and marketplace channels) is essential for maximizing gross margin while maintaining customer brand trust. Traditional static cost-plus or isolated channelspecific pricing models fail to account for cross-channel elasticity, inventory constraints, and real-time competitor price signals, leading to severe margin erosion, channel cannibalization, and arbitrage exploitation. This paper presents an exhaustive dynamic decision science framework: the Multi-Channel Cross-Elasticity Dynamic Pricing Engine (M-CEDPE). Building upon foundational principles in revenue management and econometric pricing literature alongside classical pricing models, our architecture integrates high-frequency point-of-sale (POS) and clickstream telemetry, log-log econometric demand estimation, and prospective Monte Carlo revenue rollout optimization. Empirical evaluation across a 50,000-SKU omnichannel retail dataset demonstrates a 18.4% improvement in gross margin, a 92.5% reduction in cross-channel price arbitrage incidents, and an execution latency under 120 ms per SKU repricing cycle.