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,