A 'price balance statistic' for optimizing pricing strategies: a better estimation of elasticities and cross-elasticities

Demand forecasting, price optimization and capacity controls form three major tools of revenue management. Over the past few decades, each discipline has generated a great deal of research but has typically been studied separately from the others. Yet, better understanding their relationship gives an airline the opportunity to increase its profitability. In prior work, Tim Jacobs and colleagues introduced a macro-level metric known as the ‘Price Balance Statistic (PBS)’ for evaluating the quality of a given pricing strategy and guiding a search algorithm to identify an optimal alignment between pricing structure, scheduled capacity and RM controls using marginal revenue principles. The aim of our work is to incorporate additional modeling improvements to the PBS. The current model formulation uses price elasticity as input parameters and assumes perfect independency between the different fare classes. However, in reality, a passenger demand fluctuates between classes based on differences in prices. We propose to use instrumented variable linear regression methods to obtain parameter estimates for price elasticities and cross-elasticities. This modification incorporates accurate price elasticities but also the impact of a change in one fare class on another through the cross-elasticities.