Standard revenue management (RM) methods typically work with an integrated forecast of demand and customer choice at booking class level and then maximize revenue by optimally controlling class availability. We show that the structure of the RM problem can be decomposed into two sub-problems: capacity control and price optimization for incoming customer requests. For capacity control in practice often bid prices, the dual variables to the capacity constraints, are used. Price optimization on the other hand needs forecasts of passengers’ willingness to pay. We propose to use two separate and tailored forecast models for both optimization tasks and discuss the advantages of this forecast separation. Furthermore, for the task of bid price calculation, we present a robust forecast model that is independent of booking classes and the actual control mechanism. It depends on historical booking data and bid prices only, but does not require historical fares and availabilities, which are often not available in good quality.