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- Pricing practices of football and basketball clubs in Italyel junio 1, 2023 a las 12:00 am
Abstract Revenue management is widely adopted across industries—except in the sports industry. In the context of the present study, we contact every single football and soccer club belonging to the top league in Italy—one of the world’s most successful football nations—and obtain responses from close to half of all clubs. We find that one-third of all football clubs apply revenue management, far more than basketball clubs. Basketball clubs rely heavily on fixed pricing, whereas soccer clubs use variable pricing or revenue management. Study data also suggest that perceived barriers to revenue management include a fear of alienating customers or perceived high implementation costs; since experiences of revenue management implementation are overwhelmingly positive, this study suggests that the fears of revenue management implementation are overblown. This study advances research on the practical implementation of revenue management in non-traditional industries and provides an encouragement for executives working in other industries to implement revenue management.
- Personalization @ scale in airlines: combining the power of rich customer data, experiential learning, and revenue managementel abril 1, 2023 a las 12:00 am
Abstract Recently, several macro trends have converged to provide airlines new opportunities for one-to-one digital customer engagement and personalization. Airlines have more types and volumes of data available than ever before: shopping-behavior data, data providing context on booking decisions, social media data enriching the information available on travel trends, and more. All of these can play a critical role in defining the right offers and setting the right prices for each shopping request. A plethora of advanced AI and ML techniques have become available on open-source platforms, letting players generate actionable customer insights and leverage vast amounts of existing data. New distribution technology is being deployed to allow airlines to implement real-time retailing capabilities. Consumers have been trained by the likes of Amazon, Netflix, Alibaba, and Starbucks to expect products and services tailored to their individual needs along with superior and engaging content. This paper presents different approaches to price-product personalization that have been tested in airline cases globally. It also explores how the concept of experiential learning is nicely suited to tackling scenarios in which the purchaser is well-identified as well as cases in which not much is known about the visitor except the context of the shopping session.
- Dynamic offer creation for airline ancillaries using a Markov chain choice modelel abril 1, 2023 a las 12:00 am
Abstract Customers have become accustomed to a highly streamlined and personalized experience when shopping online. While tech giants such as Apple, Amazon, and Netflix are experts in using customer information and shopping context to deliver relevant offers, airlines are falling behind in this regard with their static content and one-size-fits-all retailing approach. To meet the growing expectations of their customers, the airline industry has expressed a vision for dynamic offer creation, which will allow airlines to dynamically bundle and price a set of offers that is customized to the context of the shopping request. Realizing this vision requires significant advancements in both distribution and science. On the distribution side, these advancements will come with the adoption of the New Distribution Capability. On the science side, which is the focus of this paper, little progress has been made despite years of research. In particular, airlines still lack a tractable scientific model to dynamically create and price offer sets at scale. In this paper, we present a novel approach to solve the airline dynamic offer creation problem using a Markov chain choice model. Our model displays attractive qualitative properties—the resulting offers and prices are chosen in such a way as to discourage purchases of unprofitable offers and nudge customers towards more profitable ones. Our model naturally proposes offers that are relevant to the customer, as including irrelevant offers in the offer set leads to a reduction in revenue and ancillary purchase rates. In a simulation study with two customer segments, we find that our model significantly increases ancillary revenue over a naïve, unsegmented pricing model that mimics current state-of-the-art practice. While our studies are conducted under several idealized assumptions, they demonstrate a substantial revenue potential from dynamic offer creation in both unsegmented and segmented applications.
- Price fairness: square equity and mean pricingel marzo 14, 2023 a las 12:00 am
Abstract Prices have a leverage effect on firm profits. Prices, however, have also an impact on customer’s perceived price fairness and thus indirectly on firm’s bottom line. A growing body of literature shows this. Papers on the level of the fair price, however, are rather scarce. Based on different concepts of justice, two levels of fair prices are proposed: square equity and mean prices. I run an experiment which shows that both are considered fairer than cost-based or value-based prices. The results can be used to assess fairness implications of prices ex ante and hence complement traditional pricing approaches.
- Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotelsel marzo 11, 2023 a las 12:00 am
Abstract Demand forecast accuracy is critical for hotels to operate their properties efficiently and profitably. The COVID-19 pandemic is a massive challenge for hotel demand forecasting due to the relevance of historical data. Therefore, the aims of this study are twofold: to present an extension of the additive pickup method using time series and moving averages; and to test the model using the real reservation data of a hotel in Italy during the COVID-19 pandemic. This study shows that historical data are still useful for a SME hotel amid substantial demand uncertainty caused by COVID-19. Empirical results suggest that the proposed method performs better than the classical one, particularly for longer forecasting horizons and for periods when the hotel is not fully occupied.