The intersection of stochastic modeling and operations research techniques has allowed researchers to tackle through time and, with mathematical rigor and elegance, contemporary business problems raised with much urgency by changes in the economic environment and advances in technology. One recent application area is pricing analytics and revenue optimization. It originated with the airline industry in the 80s, when airlines pressured by deregulation and competition turned towards "revenue management" (RM) to develop new market segmentations (demand) and optimize accordingly their inventory allocations (supply). Most other industries have focused primarily inwards, on optimizing their internal processes. The democratization of the internet (and e-commerce along with it), then the growing popularity of cross-functional enterprise planning systems, urged other industries to start looking outwards as well. This propelled RM to new heights. Nowadays, every single industry (from healthcare to manufacturing, from entertainment and advertisement, to retail, from B2C to B2B) is looking at ways to integrate advanced RM concepts to its value chain. Research has led the way on this journey, uncovering subtle and relevant modeling layers including dynamic pricing, network revenue management, demand learning, choice modelling and many more. More recently, data science and technology disruptions provide us with a vivid illustration of how rich and resilient this research area is. By embracing fundamentally new issues faced by major players, such as Google, Amazon, Facebook, Uber and the like, RM adapted when needed its own sets of tools and techniques and is now a fertile playing ground for the latest developments in OR, CS, economics and statistics.