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

TL;DR

This paper introduces a machine learning model that integrates diverse data sources to improve demand forecasting in airline revenue management, leading to higher revenue predictions than traditional methods.

Contribution

It combines multiple data sources and analyzes competitor pricing to classify customer behavior, enhancing itinerary-level booking prediction accuracy.

Findings

Customer behavior can be categorized into price-sensitive, schedule-sensitive, and comfort-sensitive groups.

The model outperforms traditional time series forecasts in revenue prediction.

Using real airline data, the approach improves revenue outcomes in simulations.

Abstract

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.