This chapter surveys the state of the art in dynamic pricing and promotional strategy for online retail, integrating insights from marketing science, operations, and machine learning. We first synthesize rule-based and AI-driven pricing approaches that update prices in real time by learning demand, tracking competitors, and balancing exploration and exploitation. We then examine how individualized discounts, targeted coupons, and recommendation-linked offers blur the boundary between “pricing” and “promotion,” and outline design principles for segment- and context-specific interventions rather than one-size-fits-all markdowns. Next, we connect pricing and promotion to analytics for demand forecasting and yield management, emphasizing the feedback loop whereby price changes and promotions reshape demand and, therefore, must be embedded in forecasting models to avoid biased decisions. Classical revenue-management results and contemporary retail cases are used to illustrate inventory-aware pricing, clearance timing, and cross-channel allocation. Finally, we address consumer-side consequences—fairness perceptions, trust, and strategic waiting—and discuss governance tools (e.g., guardrails, transparency, and experimentation protocols) that sustain long-term loyalty while meeting revenue objectives. The chapter contributes a cohesive framework linking algorithms, promotional mechanics, and forecasting/yield decisions, and offers actionable guidance for retailers seeking scientifically grounded, customer-centric revenue optimization in fast-moving digital markets.