Transitioning to a Future of Intelligent Dynamic Offers Continuous Pricing, Dynamic Offer Generation and Customer-Centric Airline Retail

White Paper by The COVID-19 pandemic has caused havoc for traditional revenue management models. The Introduction typical patterns of peak and low seasons, estimated booking windows, look to book ratios as well as the A Datalex white paper on the important developments expected patterns of boom-and-bust economies no longer apply. Dealing with this major disruption in Continuous Pricing and Dynamic Offer Generation in revenue management requires innovative thinking, a great deal of flexibility and importantly, more for a future of rich airline retailing. Featuring exclusive agile and intelligence driven pricing and offers than ever before. insights from a Datalex interview with leading international authority on Dynamic Offer Generation Airlines are showing an appetite for innovation and experimentation to leap-frog competitors during and Continuous Pricing – Dr. Peter Belobaba, Principal recovery – including an acceleration of pace in Dynamic Offer & Continuous Pricing, powered by Research Scientist at the MIT International Centre of smarter decision-making and artificial intelligence. The future of airline retailing will look different Air Transportation and Director of the PODS Revenue than it did pre-COVID, and it is evident that the ability to respond to rapidly changing market and Management Research Consortium. customer demands instantaneously is key. Moreover, airlines need to explore new retailing opportunities to better distinguish themselves from their competitors. While dynamic offer

© Datalex generation (the top right corner of the IATA Dynamic Maturity Matrix (1)) is still in its infancy, airlines can invest in moving towards this in a strategic manner with dynamic product determination and dynamic price adjustments today, while preparing for a future move to full dynamic offers without the restrictions of filed fares and RBD’s.

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 01 Datalex had the privilege to interview renowned industry expert in dynamic offer generation and continuous pricing, Dr. Belobaba, Principal Research Scientist in the International Center for Air Transportation at MIT and Director of the PODS Revenue Management Research Consortium, which oversee in-depth research into advances in dynamic offer generation and continuous pricing for airlines worldwide. He is currently supervising three PHD students focused on different aspects of Continuous Pricing including ‘Continuous Pricing and Multiple Fare Products’, ‘Continuous Pricing and Dynamic Bundling Offer Generation’ and ‘Continuous Pricing and Estimation of ‘Conditional Continuous Willingness to Pay’.

In our interview, Dr. Belobaba shared his expert insights on the below topics. Here we share those Pricing and insights as well as the Datalex approach in addressing these in our strategy to support airlines to transition from dynamic product determination and dynamic price adjustments today, while preparing Dynamic Offer for a future of full dynamic offers construction with the airline firmly in control. Generation Outline Learnings from Dr. Belobaba, Principal Research Scientist in the International Center for Air Transportation at MIT • The ‘Spiral Down’ Effect • Price Influencing Factors – Market Types, Competition and Customer Segmentation • Conditional Willingness to Pay • The role of Revenue Management post-covid • Assessing the gains and influencing factors of moving to Continuous Pricing • Leveraging advanced Revenue Management and Dynamic Pricing Engines • When does Continuous Pricing work best?

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper Dr. Belobaba confirmed that when applying continuous pricing, many airlines fear that The ’Spiral Down’ competitors may just simply match or undercut their prices which have been calculated by sophisticated continuous pricing algorithms. This type of irrational Revenue Management decision by a competitor Conundrum and in the market would cause what Dr. Belobaba describes as a ‘spiral down’ scenario. If a competitor is not using Continuous Pricing but instead simply undercuts pricing to win the pricing battle causing a ‘spiral down’ effect, this would require a sophisticated pricing strategy and approach to avoid being drawn into How to Avoid it such a spiral down scenario.

DRIFT

Controls the error rate of the algorithm(s).

REASONING

Reasoning allows the system to adapt and respond to different situations.

SUPERVISED PRICING

Price elasticity needs to be measured and monitored by Revenue Management to ensure desired results

Addressing the ‘Spiral Down’ challenge is important to ensure a strong outcome from Continuous Pricing. This is where a combination of a unique blend of what Datalex calls its Innovation Pillars to Dynamic Pricing as shown in the graphic above.

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 03 Looking at only demand can be unreliable especially in extreme times such as experienced during the COVID-19 pandemic when the demand is erratic and unpredictable. Datalex addresses this by looking beyond demand and including multiple price influencing factors, such as price prediction. This approach is further strengthened with a blend of drift detection, reasoning and supervised pricing.

Drift Detection Reasoning Supervised Pricing Data changes over time and may result in a Reasoning allows the system to consider Price elasticity needs to be measured and degrading predictive performance. The ability to different scenarios to determine if the price being monitored by Revenue Management to ensure detect unknown or hidden relationships results suggested by the model is optimal for both the desired results. Revenue Management needs in better predictive performance and therefore constomer and the airline, and therefore has a to monitor the impact of prices in real time better pricing. Drift detection helps to ensure higher conversion. and modify accordingly to ensure the desired the integrity of the predictive performance of the results increase customer satisfaction and pricing model. conversion. This also provides Revenue Management with concrete results that establishes trust and confidence in dynamic pricing. At reaching the point of confidence, supervised pricing can be switched off.

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 04 Dr. Belobaba explains that the type of market, the number of competitors and also the type of Price Influencing competitors are important price influencing factors. Revenue gains of Continuous Pricing depend on algorithms, baseline number of price points, and the competitive situation. Tests carried out by Factors the PODS Consortium at MIT have shown first-mover revenue gains at the competitor’s expense, primarily by undercutting the existing fare structures and stealing market share. (3) Moreover, by taking Market Types, Number of Competitors, Type of Competitors customer segmentation together with the competitive environment, this allows for more and Customer Segmentation sophisticated Continuous Pricing. This means applying more granular data and models to allow airlines to make smarter pricing decisions with more refined customer segmentation and by taking into account the competitive landscape.

Leveraging multiple price influencing factors is key to success. By considering a very extensive set of price influencing factors across market types, competitive environment, customer segmentation and the purchasing context, the effectiveness of continuous pricing is improved. Leveraging data and pricing models to allow airlines to make better decisions by having a more refined customer segmentation based on the context of the request and by taking into account the competitive landscape. By doing so, this enables airlines to maximise the associated increased revenue generated from continuous pricing.

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 05 Central to Dr. Belobaba’s and the MIT / PODS Consortium research is the concept of ‘conditional willingness to pay’, beyond the traditional thinking around ‘willingness to pay’. Willingness to

In our discussion, Dr. Belobaba described the example of a Boston-Orlando route and market versus Pay (WTP) the Boston-Chicago route and market, which presents entirely different competitive and customer environments. An LCC competitor in the Boston-Orlando market could have a much bigger impact ‘Conditional Willingness to Pay’ and ‘Maximum Willingness to Pay’ on prices than an LCC competitor in the Boston-Chicago market, because of the different types of customers with different shopping behaviour and different willingness to pay (WTP). This is referred to as “conditional willingness to pay”, i.e. the willingness to pay depends on the market conditions, competitive landscape and booking scenario. Conditional WTP goes down when another competitor enters the market. In extreme cases, if an LCC enters the market and offers a very low fare, then the conditional WTP goes down even more.

Dr Belobaba also outlines that another distinction is between maximum WTP (which most researchers have focused on) and conditional WTP. Specifically, assuming a customer will pay their maximum ‘willingness to pay’ is unrealistic if they have competitive alternatives with prices much lower than their maximum WTP. As long as the customer knows of the cheaper alternative, they will not pay more, all else being equal. Their WTP is thus conditional on what alternatives are available in the market.

Many airline revenue management systems have further developed demand forecasting with price elasticity and WTP estimates and parameters. Airlines can deal with the above scenario by adjusting the RM demand forecast and WTP estimates in a market given the entry of an LCC into that market, $$$ which then adjusts network optimisation to generate different bid prices that feed the DPE (Dynamic Pricing Engine) or continuous pricing mechanism.

To address a scenario such as the above, it is important to be able to support multiple pricing models, with an extensive set of price influencing factors that can all be switched on or off for different scenarios. The idea is not only to be able to switch price influencing factors on or off, but also to apply different weighting to price influencing factors. So even if it is not completely switched off, a price influencing factor could have more or less influence depending on the weighting.

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 06 Dr. Belobaba is clear that Revenue Management is not going away, it is being reimagined. Airlines The Role of have spent many years and invested hundreds of millions in the development of their smart Revenue Management systems. Now airlines are taking the covid period as an opportunity to pause and Revenue invest further to set themselves up to be better positioned technologically when the demand returns. Essentially, the revenue management system will continue to be the primary driver of demand forecasting and optimisation. The marginal cost or bid price to base the dynamic price adjustment Management should come from the existing revenue management systems. The recommendation from Dr. Belobaba is to use the data from the Revenue Management systems (demand forecast, optimisation Post-Covid data, etc.) in a smart way by leveraging the airline’s DPE (Dynamic Pricing Engine). Replacing Existing Revenue Management is not the Goal Moreover, as Dr Belobaba points out, the beauty with advanced Revenue Management systems, is that they have an architecture whereby forecast parameters can be changed very quickly and airlines can experiment with reducing the historical period down to a few weeks or days using more Historical Data in a Post-COVID world sophisticated statistical tools, algorithms or artificial intelligence based on anticipated volume, expected arrival patterns and willingness to pay. Advanced revenue management systems can allow At the time of writing this paper (June 2021), we are over a year into the COVID-19 this level of parameter manipulation and this will enable the airline to detect early blips and early pandemic. Airlines need to shift from forecasting bookings. As Dr Belobaba explains, the airlines need to estimate based on something and the best models that rely primarily on historical data to RM systems have parameters to change volume of demand, leisure / business mix, expected arrival ones that analyse real-time demand. Airlines have patterns and so on, as well as supplementing that with analytics from external data. been responding by changing how they forecast demand and these changes are shaking up the traditional revenue management approach. Being able to leverage the data coming out of the Revenue Management systems in a smart and While there is talk of eliminating historical data flexible way is key as is the ability to take various feeds from the airline’s existing RMS, like (historical) entirely, there is still value in leveraging signals sales data and/or booking profiles to generate demand forecast, or take demand forecast data from from the past such as the seasonal influences on the airlines RMS. This can be combined with actual sales data and various price influencing factors like demand and weekdays vs weekend for example. events, weather, competitor data, customer segment, etc. This brings the opportunity, as well as the challenge, to incorporate newly relevant New COVID-19 related price influencing factors like governmental restrictions, trust level, vaccination signals and blend them in. Moreover, this is percentage, COVID test requirements and so on should also be taken into account as price influencing where the importance of Continuous Pricing factors, having an impact on customer’s willingness to pay. comes in – as it aims to overcome the typical lag that happens when relying on historical trends and instead dynamically adjust fares in response to “real-time” demand signals. (2)

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 07 Dr. Belobaba points out that the gains of moving There is no guarantee that putting in more to continuous pricing are dependent on many price points will automatically lead to increased factors. To start with it is dependent on the revenue. That’s only if you can assume that number of fare classes the airline uses as a customers come in exactly the inverse baseline today. Some of the (older) research order of willingness to pay, which is not what’s was done based on 6 fare classes. More recent happening in the real world. However, Assessing the research was based on a more realistic baseline of increased granularity of price points can lead to 12-18 fare classes. revenue gains, as long as incremental revenues Gains of Moving of both stimulated demand and sell-up exceed revenue losses due to buy-down from existing to Continuous higher fares (3). Pricing, and the Continuous Pricing unlimited price points There is also a definite ‘first mover advantage’ First Mover to be gained by being first to market ahead of Price your competition with Continuous Pricing. Dr. Belobaba, makes the case clear for Continuous Advantage Pricing – quoting up to 9% revenue gains for a first mover advantage.

Revenue Demand

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 08 When Does According to Dr. Belobaba, continuous pricing works best if demand is reaching capacity or is Continuous greater than capacity. As airlines prepare for the Pricing work post-COVID recovery and an eventual surge in bookings due to pent up demand, this will best? become increasingly important. However it is also important that airlines can adapt pricing models regardless of demand, high, medium or low.

This reinforces the need to support multiple price Dr Belobaba is clear that airlines or Leveraging influencing factors allowing for an extended set technology providers do not necessarily have of concepts and features that can be enabled/ to go back to the drawing board to make their Advanced disabled when shaping the pricing model. This Dynamic Pricing Engine “COVID proof”. As Revenue allows for pricing models that are not necessar- described earlier, the airlines that are using the most ily dependent on demand and capacity such as advanced revenue management systems have an Management we are experiencing during the pandemic, when architecture that allows Revenue Management and Dynamic demand is low. executives to change parameters on the spot to overcome the irrelevance of historical sales data Pricing Engines for demand forecasting. This can be further $$$ enhanced by using more sophisticated statistical $$$$ tools or artificial intelligence to pick up early blips and early bookings.

As Dr Belobaba explains, ultimately what really matters for a demand forecast for a specific flight is: what is the anticipated volume, what is the expected arrival pattern and what is the expected willingness to pay. These can be manipulated by the business user based on what they are observing and the latest booking patterns they see (for example during COVID everybody is booking much closer to departure).

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 09 A report from McKinsey estimates AI alone has the potential to deliver global economic The Artificial activity of around $13 trillion by 2030 (5), powering new customer experiences across all engagement channels. Enabling airlines to cross-sell, upsell and serve in the moment of need using AI to empower the Intelligence customer interaction, leveraging data for a digital-first future. While the power of artificial intelligence to drive highly sophisticated dynamic offer generation and continuous pricing is understood – Dr. Belobaba points out that Revenue Management analysts need to be able to understand the impact Opportunity from artificial intelligence and want to be able to make pricing adjustments accordingly.

This challenge can be overcome by ensuring that A/B testing and reporting are essential tools available to Revenue Management and that they are also prerequisites for an intelligent Dynamic Pricing Engine. This allows Revenue Management business users to monitor the results of artificial intelligence based continuous pricing algorithms and to create confidence in same. Proven revenue uplift is key to ensure wider adoption going forward.

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 10 Dynamic offer generation (the top right corner of the IATA Dynamic Maturity Matrix (1)) is still a future target. At the MIT PODS Consortium, they are looking at applying continuous pricing to dynamic Reinforcing the offer generation combined with optimisation of which bundles / product determination to provide for specific customer segments in order to maximise revenue. Dr. Belobaba describes this as the holy Crawl, Walk, Run grail for airlines but he also points out that the research is not quite there and the models are still in development. approach to Full Consumer reaction as well as regulatory reactions are yet to be determined. Some local regulations don’t allow this, some dictate that airlines must publish (some) fares that are accessible to the Dynamic Offers public. The IATA Dynamic Offers group is taking the lead in this area and is already in the process of negotiating with the applicable governmental bodies. Moreover, some PSS and GDS and most downstream systems like revenue accounting, are not yet ready for continuous pricing without filed fares and RBDs. Finally, some of the IATA resolutions and standards need to be adjusted (e.g. historical fares). Again, the IATA Dynamic Offers group is taking the lead on this.

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Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 11 This reinforces the need for a crawl-walk-run on the journey to full dynamic offer construction in the top right-hand corner of the IATA Dynamic Offers maturity matrix. Airlines need to strategically invest in steps towards the “upper right corner”, but the result of each of those steps must be something that an airline can take into production and that generates revenue. Therefore, adding incremental revenue at every step and moving the airline closer to true and full Dynamic Offer control. At their pace and with solid and proven results at each stage – to facilitate and validate the move to the next. 2022 2023 H1 2021 H1 H2 2021 H2

Price Determination Dynamic Price Adjustments Continuous Pricing Full Datalex Dynamic Pricing > Powered by Digital Configurator > Data-Driven Continuous pricing Engine (DPE) through Datalex Dynamic Pricing > Continuous Pricing through DPE > Empower business users to beat Engine the limitations of a staggered demand curve > Artificial Intelligence/ > No RBD or reference fare Machine Learning > Step up to continuous pricing and > ONE Order unlimited price points > Leverage AWS partnership > Single point of truth for all channels

Product Determination Contextualisation Micro segmentation > Data on the context of offer search > More granular customer segment Data Segmentation > Data on the customer segment Personalisation > Personalised customer data from CDW, Google Analytics etc

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 12 Legacy business processes and technology are limiting the airline’s ability to identify, set and Preparing for a future of full adjust estimates of “willingness to pay” and associated pricing in real-time. Airlines do not need to be constrained by 26 inventory buckets anymore and are investing in new systems to move faster than dynamic offer construction, the traditional PSS permits.The opportunity to re-capture 2 – 5 points of lost revenue and up to 10 putting the airline firmly in post-COVID is significant. control Clearly, Continuous Pricing and Dynamic Offer Generation will bring substantial benefits in terms of revenue gains for the airline both as a first mover and a fast follower. The vast and rapidly evolving AI and Machine Learning techniques will allow airlines to do more sophisticated product and price According to research at MIT, when one of four determination in real-time or close to real-time. The opportunity is immense and it is important to get airlines implements Dynamic Offer Generation, this right. Airlines need to react faster in market than ever before and have the control to quickly adapt it can increase its total net revenue by up to 2.6% pricing to engage customers and drive more revenue with faster, smarter data driven offers. through ancillary bundling alone and up to 12% in combination with dynamic flight pricing. Most of these dynamic flight pricing gains are attrib- At Datalex, we have mapped out a clear path to full Dynamic Offer Construction via our roadmap utable to undercutting the existing fares offered and vision for the Datalex Dynamic product. We are strategically investing in steps towards the by airlines with traditional RM systems. When all “upper right corner”, but the result of each of those steps must be something that an airline can take into four airlines use DOG, their revenue increases by production and that generates revenue. Bringing airlines on a journey to incrementally enrich the up to 0.9% through bundling alone and 7% with dynamic pricing. (6) shopping experience with a compelling service and price, with timely and logical advances in price and product determination, ultimately edging closer towards full dynamic offer generation – and all at the right pace for the airline and its digital retailing strategy.

We are navigating this journey together with our airline customers, with a market-leading offering to give airlines a competitive edge in this space. To speak to our experts in Continuous Pricing and Dynamic Offer Generation, do not hesitate to get in touch.

Ryan Estes, VP Technology, Datalex - [email protected] Fred van Toorn, Senior Product Manager, Datalex Dynamic - [email protected] Conor O’Sullivan, Chief Product Officer, Datalex - [email protected]

Website: datalex.com/datalex-dynamic

Transitioning to a Future of Intelligent Dynamic Offers | Datalex White Paper 13 Datalex: Datalex is a market leader in digital commerce for travel retail. Datalex provides airlines with unique products to drive revenue and profit as digital retailers. Our products offer airlines the ability to deliver a competitive and differentiated airline retail experience on every device, across every sales channel and at every touchpoint in the customer journey.

Datalex Dynamic Datalex Direct Datalex Merchandiser Datalex NDC Intelligent Price and Product Determination Powers Next Generation Omni-Channel Unlock New Revenues Beyond the Seat Enhanced Retailing Across All Channels Revenue for the Digital Airline

Datalex’s products and platform operate at scale with over one billion shoppers annually, covering every corner of the globe and used by some of the world’s most innovative airline retail brands. Datalex’s customers include, Air China, JetBlue, Hainan Group, SAS, Aer Lingus, Air Transat, Edelweiss and Trailfinders. The company is headquartered in Dublin, Ireland, and maintains offices across Europe, the USA and China. Datalex is a public company and is listed on Euronext Dublin (DLE).

White paper Contributor via Interview: Dr. Peter P. Belobaba International Center for Air Transportation Massachusetts Institute of Technology Peter P. Belobaba is Principal Research Scientist in the International Center for Air Transportation at the Massachusetts Institute of Technology (MIT), where he teaches graduate subjects on The Airline Industry, Airline Management, and Air Transportation Operations Research. He is Program Manager of MIT’s Global Airline Industry Program and Director of the PODS Revenue Management Research Consortium. Dr. Belobaba holds an MS in Transportation and a Ph.D. in Flight Transportation Systems from MIT. He is a lead author and editor of the recently released book, The Global Airline Industry, 2nd Edition. Dr. Belobaba has been involved in research related to airline economics, pricing, network planning, competition and revenue management since 1985. His doctoral dissertation entitled, “Air Travel Demand and Airline Seat Inventory Management”, is widely recognized as the first Ph.D. thesis published on the topic of airline yield management. He has worked as a consultant on the development, testing and implementation of pricing, revenue management and distribution systems at over fifty airlines and other companies worldwide. He has also published numerous articles in a variety of journals, including Airline Business, Operations Research, Transportation Science, Journal of Revenue and Pricing Management, and Journal of Air Transport Management. In 2016, Dr. Belobaba was awarded the INFORMS Impact Prize for his “pivotal role in the creation and wide-spread adoption of revenue management”.

Sources: 1. IATA Dynamic Maturity Matrix : https://www.iata.org/contentassets/0688c780d9ad4a4fadb461b479d64e0d/iata-dynamic-offers-brochure_airs2019.pdf 2. Dynamic pricing mechanisms for the airline industry: a definitional framework – June 2018 - Michael D. Wittman & Peter P. Belobaba 3. PODS Consortium Research Update: Continuous Pricing Mechanisms and Impacts Dr. Peter P. Belobaba Alexander Papen and Bazyli Szymanski 4. executiveeducation.wharton.upenn.edu/thought-leadership/wharton-at-work/2018/04/dynamic-pricing Z. John Zhang, PhD, Tsai Wan-Tsai Professor; Professor of Marketing; Director, Penn Wharton China Center 5. Notes from the AI frontier: Modeling the impact of AI on the world economy mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy 6. Airline Revenue Management with Dynamic Offers: Bundling Flights and Ancillary Services, Kevin K. Wang, MIT, May 2020

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