Identifying and Responding to Outlier Demand in Revenue Management

Revenue management strongly relies on accurate forecasts. Thus, when extraordinary events cause outlier demand, revenue management systems need to recognise this and adapt both forecast and controls. State-of-the-art systems rely on analyst expertise to identify outlier demand both online (within the booking horizon) and offline (in hindsight). In light of the partial nature of revenue management data and censoring effects from inventory controls, so far, there exists little research on automating the detection of outlier demand. To remedy this, we propose a novel approach, which detects outliers using functional data analysis in combination with extrapolation via time-series forecasting. We evaluate the approach in a simulation framework, which generates outliers by manipulating the demand model. By evaluating the full feedback-driven system of forecast and optimisation, we generate insight on the asymmetric effects of positive and negative demand outliers in light of revenue management heuristics that do or do not account for customer choice. Furthermore, we quantify the value of both online and offline outlier detection. We show that identifying instances of outlier demand using our methodology, and adjusting the forecast in a timely fashion, substantially increases revenue compared to what is earned when ignoring outliers.