Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

Employing customer information from one of the world's largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer's acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Hotel revenue management: usingdata science to predict booking cancellations

In the hotel industry, demand forecast accuracy is highly impacted by booking cancellations. Trying to overcome loss, hotels tend to implement restrictive cancellation policies and employ overbooking tactics which in turn reduces the number of bookings and reduces revenue. To tackle the uncertainty arising from cancellations, models for the prediction of a booking's cancellation were developed. Data from hotels' reservations systems was combined with data from other sources (events, holidays, online prices/inventory, social reputation and weather). Despite data class imbalance, concept drift, and dataset shift problems, it was possible to demonstrate that to predict cancellations of bookings is not only possible but also accurate. Moreover, it helped to better understand what the cancellation drivers can be. In order to assess the models under real conditions, a prototype was developed for field tests allowing to evaluate how an automated machine learning system that predicts booking’s cancellations could be integrated into hotels' systems. The model's performance in a real environment was assessed, including the impact on the business. The prototype implementation enable an understanding of adjustments to be made in the models so that they could effectively work in a real environment, as well as fostered the creation of a new measure of performance evaluation. The prototype enabled hoteliers to act upon identified bookings and effectively decrease cancellations. Moreover, results confirmed that booking cancellation prediction models can improve demand forecast, allowing hoteliers to understand their net demand, i.e., current demand minus predicted cancellations.Na indústria hoteleira, a precisão da previsão da procura é altamente impactada pelos cancelamentos de reservas. Na tentativa de mitigar as consequências dos cancelamentos, os hotéis tendem a implementar políticas de cancelamento restritivas e táticas de "overbooking", o que, por sua vez, reduz o número de reservas e a receita. Para combater a incerteza decorrente dos cancelamentos, foram desenvolvidos modelos capazes de prever a probabilidade de cada reserva vir a ser cancelada. Neste desenvolvimento foram utilizados dados de oito sistemas de gestão de reservas de outros tantos hotéis, conjuntamente com dados de outras fontes (eventos, feriados, preços/inventário "online", reputação social e clima). Apesar dos problemas de desequilíbrio de classe de dados, desvio de conceito e variação de distribuição entre variáveis ao longo do tempo, foi possível demonstrar que prever cancelamentos de reservas não é apenas possível realizar, mas que é possível de fazer com elevada precisão. A elaboração dos modelos ajudou ainda a compreender os fatores que influenciam o cancelamento. Para avaliar os modelos em condições reais, foi desenvolvido um protótipo, o qual permitiu avaliar como um sistema automatizado baseado em aprendizagem automática para prever os cancelamentos de reservas pode ser integrado nos sistemas dos hotéis. Este protótipo permitiu ainda avaliar o desempenho dos modelos num ambiente real, incluindo o seu impacto na operação. A implementação possibilitou também compreender os ajustes a serem feitos aos modelos para que pudessem efetivamente trabalhar num ambiente real, bem como fomentou a criação de uma nova medida de avaliação de desempenho. O protótipo permitiu que os hoteleiros agissem sobre as reservas identificadas e efetivamente diminuíssem os cancelamentos. Para além disso, os resultados confirmaram que os modelos de previsão de cancelamento de reservas podem melhorar a previsão de procura, permitindo que os hoteleiros compreendam melhor a sua procura líquida, ou seja, a procura atual menos os cancelamentos previstos

Pilot\u27s Handbook of Aeronautical Knowledge, 2016

The Pilot’s Handbook of Aeronautical Knowledge provides basic knowledge that is essential for pilots. This handbook introduces pilots to the broad spectrum of knowledge that will be needed as they progress in their pilot training. Except for the Code of Federal Regulations pertinent to civil aviation, most of the knowledge areas applicable to pilot certification are presented. The Pilot’s Handbook of Aeronautical Knowledge provides basic knowledge for the student pilot learning to fly, as well as pilots seeking advanced pilot certification. For detailed information on a variety of specialized flight topics, see specific Federal Aviation Administration (FAA) handbooks and Advisory Circulars (ACs). Occasionally the word “must” or similar language is used where the desired action is deemed critical. The use of such language is not intended to add to, interpret, or relieve a duty imposed by Title 14 of the Code of Federal Regulations (14 CFR). It is essential for persons using this handbook to become familiar with and apply the pertinent parts of 14 CFR and the Aeronautical Information Manual (AIM). The AIM is available online at www.faa.gov. The current Flight Standards Service airman training and testing material and learning statements for all airman certificates and ratings can be obtained from https://www.faa.gov