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Contact Title Institution Abstract
Bazyli Szymański Continuous Pricing with Multiple Fare Quotes MIT Continuous pricing enables fare quotation from a continuous range, as opposed to the current industry standard limiting airlines to a fixed set of published price points. Extending previous work in the PODS consortium, we introduce a framework for continuous pricing with multiple fare quotes through product differentiation and fare quote segmentation. Customers may be offered multiple continuously priced products to choose between (product differentiation), and their offers may differ based on segment identification at purchase request. PODS simulations are used to illustrate the potential for revenue gains from applying continuous pricing methods relative to traditional class-based RM and to investigate their competitive impacts.
Kevin Wang Airline offer optimization with the Markov chain choice model MIT Airlines today distribute and price bundles of flights and ancillaries (i.e., Fare Families and Branded Fares), as well as a la carte ancillaries, using filed fares. New distribution technologies have enabled airlines to distribute an offer set containing multiple offers, each of which consists of a set of atomic products and a single offer price. The generation of prices for each offer – which depend on the other alternatives in the offer set – represents a new optimization problem in airline RM. In this talk, we discuss how the recently proposed Markov chain choice model (MCCM) could be used to jointly construct and price bundled offers. The MCCM is a flexible choice modeling framework that can be used to represent any random utility model and allows for efficient computation of optimal prices through a series of univariate optimizations. We show how MCCM prices differ from myopic, a la carte pricing (where each offer in the offer set is priced independently) and how MCCM can provide a revenue uplift. We also illustrate how the parameters for the MCCM can be estimated from historical data via maximum likelihood estimation.
Emma Frejinger To be received Ivado Labs + Air Canada To be received

Michael Wu and

Ross Winegar

What Airlines can Learn from Retailers About Demand Forecasting PROS 2020 was a year of turbulence and disruption, especially for the travel industry. Drastic changes in travel behavior have greatly compromised airlines' ability to forecast future demand and therefore optimize revenue. Rather than relying on long histories over the past years, airlines must monitor the recent histories more carefully to understand the demand trajectory of the near future. This temporal squeeze significantly reduces the data that airlines can leverage and will increase the uncertainty of our demand forecast. To improve our forecast’s confidence bound, airlines must learn from the retailers and start leveraging a wider variety of data sources as predictors of future bookings. We constructed a model that leverages flight search data and found that this shopping data captures substantial information about people’s intent to book. This is illustrated by the fact that even a simple structured linear model is able to predict bookings with a high degree of accuracy (i.e. ~0.8 as measured by Pearson’s correlation coefficient). As more people are vaccinated, airlines that can augment the shorter relevant history creatively using shopping data will regain their capacity to forecast demand sooner and recover from the pandemic faster.
Ravi Kumar Dynamic Pricing of Ancillaries using Reinforcement Learning PROS Ancillaries have become a key driver for revenue growth for travel industries. Traditionally, pricing and offer generation for ancillary items have been managed using static business rules. In such scenarios, where historical prices show very little or no variation, typical methods of estimating purchase probabilities and then finding the optimal price are not applicable. In this study, we develop practical approaches for dynamic pricing of ancillaries based on reinforcement learning ideas. We propose a contextual bandit model for dynamic pricing of ancillaries considering the trip and customer features. The pricing setting presents significant challenges to the application of the multi-armed bandit framework since the arms are highly correlated. To capture correlation across arms, we consider a Bayesian logistic bandit framework and use Variational Bayes methodology to construct fast and scalable algorithms for this setting.
Jonas Rauch Scenario-driven RM using simulation-based reinforcement learning PROS Standard RM optimization methods such as dynamic programming make strong assumptions about the demand arrival process and are therefore not well-suited to deal with uncertainty about the demand distribution or correlation across the booking horizon. One way around this is a scenario-driven approach, but the corresponding optimization problem cannot be solved using dynamic programming. Instead, we use simulation-based reinforcement learning to approximate the value function and corresponding bid prices. Because standard RL methods such as Monte-Carlo or Temporal Difference (TD) learning do not lead to satisfying results in this context, we propose a modified TD learning update that aims to improve the quality of the bid price rather than the quality of the value function estimate.
Bertalan Juhasz Fare level optimization with continuous demand-revenue curves and bid prices Finnair In an ideal revenue management world, airlines could always offer the optimal continuous price to customers to maximize revenue. Currently, however, most airlines can only offer a set of fixed fares which results in some lost revenue, therefore it is important to have optimal fare levels which minimize this lost revenue. We present a novel method of fare level optimization which does not use previous bookings, does not use an aggregated price-demand curve (without time dimension), does not need the minimum and/or maximum fare(s) to be set in advance manually, and does not assume that passengers with higher willingness-to-pay come after passengers with lower willingness-to-pay. Instead, it uses the daily forecasted continuous demand-revenue curve (obtained from the forecasted price-revenue curve) to first obtain the optimal continuous price to offer which is at the point where the tangent of the curve is equal to the bid price; this point also tells us what the expected daily optimal demand and optimal revenue would be at this optimal continuous price. However, we only have fixed fares therefore we cannot offer exactly this price. Instead, we can offer a "mix" (i.e. linear combination) of two fixed fares, one higher and one lower than the optimal continuous price, such that we get the same demand which we would get with the optimal continuous price; this way the demand i.e. booking intake would follow the same optimal path as with the optimal continuous price. However, the revenue that we get this way is slightly less than what the optimal revenue with the optimal continuous price would be. We can then find a set a fare levels which minimizes this lost revenue aggregated across all future departure dates and booking dates. It should be a subject of further research if such "mixing" of fares is justified in this optimization context.

Thomas Fiig and

Mike Wittman

Demand Forecasting in Times of Change – Lessons Learned from a Year into the Pandemic  Amadeus The COVID-19 pandemic has significantly disrupted the traditional paradigm of how revenue management systems (RMS) use historical data to forecast future demand. To avoid polluting the historical database with unreliable or irrelevant observations, many airlines froze their demand forecasts and relied solely on manual interventions to steer flights. In this talk, we discuss the lessons learned from adapting an RMS to an environment where historical data was no longer reliable. We describe the methodology of a newly-developed forecasting concept that rapidly adjusts forecasts based on as little as a few months of live sales data, and discuss how separating forecast components into two categories – resilient and volatile – allowed us to ensure forecast stability while enabling adaptivity to the latest trends. We demonstrate how our method reduces forecast error using actual airline data, and discuss learnings from deploying this concept into production. Finally, we discuss how we see the future of demand forecasting in light of this changing business environment.
Jochen Gönsch How much to tell your customer? – A survey of three perspectives on selling strategies with incompletely specified products University of Duisburg-Essen Today’s technology facilitates new selling strategies. One increasingly popular strategy uses incompletely specified products (ICSPs). The seller retains the right to specify some details of the product or service after the sale. The selling strategies’ main advantages are an additional dimension for market segmentation and operational flexibility due to supply-side substitution possibilities. Since the strategy became popular with Priceline and Hotwire in the travel industry, it has increasingly been adopted by other industries with stochastic demand and limited capacity as well. It is actively researched from the perspectives of strategic operations management, empirics, and revenue management. This talk first describes the application of ICSPs in practice. Then, we introduce the different research communities that are active in this field and relate the terminology they use (e.g. opaque selling, flexible products, upgrades). The main part is an exhaustive review of the literature on selling ICSPs from the different perspectives. We see that strategic operations management has described advantages of ICSPs over other strategies in a variety of settings, but also identified countervailing effects. Today, empirical research is confined to hotels and airlines and largely disconnected from the other perspectives. Operational papers are ample, but mostly concerned with the availability of ICSPs. Research on operational (dynamic) pricing is surprisingly scarce.
Tim Yuxuan Lu

Conditioned Sell-up Rate Estimation and Demand Forecasting under Unrestricted Fare Structures


Unlike in a fully restricted fare structure where passengers voluntarily purchase higher fare classes, passengers facing a fully unrestricted fare structure always choose the lowest available fare across airlines, creating spiral-down problems for sell-up estimation and strong competitive feedback effects for forecasting. This presentation examines the shortcomings of the current WTP-based Q-forecasting methodology under a fully unrestricted fare structure and describes an alternative approach for conditional forecasting and sell-up estimation. The proposed conditional forecaster generates forecasts explicitly conditioned on demand volumes and lowest available fare class information from all airlines in the market. Initial proof-of-concept experiments have shown promising accuracy improvements over Q-forecasts.

Yanik Lacroix Torrent

Challenges from the Pandemic in RM & Pricing


COVID-19 has brought many challenges to revenue management and pricing teams. The WestJet team will discuss some of the tools and techniques that they developed in order to meet these challenges. The rapidly changing market conditions led to a faster response and implementation times, and provided an opportunity to rethink old heuristics and approaches and implement new models and processes.

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