A machine learning approach to itinerary-level booking prediction in competitive airline markets

Demand forecasting is extremely important in revenue management. After all, it is one of the inputs to an optimisation method which aims to maximise revenue. Most, if not all, forecasting methods use historical data to forecast the future, disregarding the 'why'. In this paper, we combine data from multiple sources, including competitor data, pricing, social media, safety and airline reviews. Next, we study five competitor pricing movements that, we hypothesise, affect customer behaviour when presented with a set of itineraries. Using real airline data for ten different OD-pairs and by means of extreme gradient boosting, we show that customer behaviour can be categorised into price-sensitive, schedule-sensitive and comfort ODs. Through a simulation study, we show that this model produces forecasts that result in higher revenue than traditional, time series forecasts.