  Demand forecasting is extremely important in revenue management. After all,
it is one of the inputs to an optimisation method which aim is to maximize
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
hypothesize, affect customer behavior when presented a set of itineraries.
Using real airline data for ten different OD-pairs and by means of Extreme
Gradient Boosting, we show that customer behavior can be categorized 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.
