The idea of dynamic pricing – optimizing prices based on demand and propensity to buy – is not a new concept. For centuries, businesses have implemented this strategy to adjust prices for products and services based on customer demand. Revenue leaders have adopted this across many industries, including hospitality, tourism, entertainment, retail, energy, public transportation, and commercial airlines with the goal to maximize sales volumes and product or service value by stimulating pricing urgency and market demand.
While the retail and entertainment industries have excelled in innovating their dynamic pricing models and technology, the airline industry has been left behind with legacy methodologies that are no longer able to keep up with today’s volatile market and ever-changing landscape. With market demand fluctuating, historical reports and sequential modeling used today can only tell a fraction of the story. And without the proper context, airlines are stuck in the past, lagging behind other industries and far from realizing the full revenue potential of dynamic pricing.
Many airlines still rely on static pricing, which uses a limited number of price points tied to the reservation booking designators (RBD) which are then filed through ATPCO. To provide their travelers with more optimal offers, some airlines started to introduce continuous pricing, offering more gradual prices. Lufthansa Group was amongst the first airlines to serve continuous priced offers on their direct and New Distribution Capabilities (NDC) channels in 2020 and immediately saw an increased revenue and conversion rate. However, with the constraint and heavy reliance for this industry on filed fares and RBD, there are comparatively few successful initiatives that involve true dynamic pricing applied to all sales channels.
On top of this, ancillary revenues – those generated through extra baggage, in-flight refreshments, internet access, seat selection, etc., which bring the industry around $55 billion according to McKinsey – are often managed through separate and isolated IT systems, meaning airlines struggle to understand how changes in dynamically priced fares also impact ancillary sales and total revenue optimization.
As long as outdated systems and obsolete methodologies remain in place, airlines will continue to fall behind other industries in maximizing revenue potential. Technological advancements, alongside successes in other industries, indicate the time is right for airlines to unlock the potential of dynamic pricing for their sales channels. This industry needs to break free from legacy technology constraints and start implementing optimal pricing strategies that take into account how decisions such as price, offer, channel or customer may impact the business outcomes and revenue performance. So what is the next step to transforming airline pricing?
Artificial Intelligence (AI) enables a transformation in airlines’ commercial performance and customer experience. Specifically, deep learning – a cutting-edge form of AI that uses neural networks trained to perform specific tasks under different conditions – creates context by looking at past behaviors, identifying good behavior versus bad behavior, and rewarding good behavior. This results in reduced forecasting errors, allowing analysts to rapidly respond to changes through added context (i.e. search data, ancillary revenue, cargo capacity, etc.). This revolutionary technology can find its path to desired business and revenue outcomes by correlating vast amounts of data, even in environments where data is sparse or noisy, a very real situation experienced by the travel and transportation industry today.
Dynamic Pricing with Deep Learning
Airlines need to sell the right product to the right customer at the right time, in the right channel, and at the right price. When embracing advanced deep learning technology, it provides automated, AI-driven revenue management capabilities that maximize airline profitability and total revenue optimization.
When it comes to dynamic pricing, deep learning and cloud development empower real-time customer segmentation and react much faster to any market change or surge in demand or commission rates. The Revenue Operating System puts into action AI-driven revenue management using deep learning to make predictions directly from context such as market forces, competitive forces, customer forces, and network changes.
Harnessing the Power of Data
Through the adoption of advanced AI, digital-first airlines are able to harness the power of data, going beyond historical data and leading the charge in dynamic pricing and other commercial decisions. With The Revenue Operating System, airline analysts can discover similarities between markets, competitors, leading demand signals, and events.
Identifying such signals before they are clearly visible in data-sparse subsets of the airline network helps focus attention where it’s needed most. With the right insights readily available and continuously updated, airline teams can, in real-time, start to resolve complex questions that used to be answered with guesswork or tribal knowledge.
Dynamic pricing powered by deep learning is the key to creating optimal offers for airlines. And as part of FLYR’s total revenue optimization ecosystem, commercial teams are now able to automate pricing decisions in real-time, optimize total revenue including ancillaries, create personalized offers optimizing customer conversions and lifetime value, create trusted load forecasts to optimize capacity plans, direct marketing spend and energy towards high yielding returns, and confidently sell cargo capacity earlier.
Charles Ruesch, Head of Offer & Distribution at FLYR
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Flexible pricing is the way of the future, says ACI – and Veovo
Airport charges, a much-debated topic between airport and airline industry bodies, have again come to the forefront as the industry rebuilds from the upheaval of the last few years.
Airport Council International (ACI) strongly advocates the shift from heavy-handed regulation, arguing that cost-based pricing should be supplemented by responding to market signals and the competitive landscape. In other words, charges that reflect passenger and airline needs while addressing environmental impacts, such as noise and pollution. And we agree.
As a company that supports airports globally with operations and revenue management, we’ve seen first-hand the issues that can arise from a strictly cost-recovery-based approach. One of the key problems is that it does not reflect recent significant changes in the industry, such as the commercialisation and privatisation of airlines and airports, new customer segments, and the resulting need for varied airport services.
While the airport body continues to push for a modern policy framework on charges, there’s still plenty that operators can do today to get more from their aeronautical pricing and revenue management practices. And many already are.
Here are some examples of airports already reaping the benefits of creative pricing and incentives as a lever to improve their competitiveness, encourage more efficient use of capacity and reduce environmental impacts.
Incentivising for growth performance
As operators look to restart routes and recover traffic, they must find new ways to attract carriers. The primary way airports can do this is by innovating aeronautical charges with new schemes and differentiated tariff structures. Some standouts in tariff creativity include:
Dublin Airport. Following the global financial crisis, Dublin’s operator DAA created a selection of long and short-haul growth incentives to revive traffic and build a healthy transatlantic network. The schemes provided rebates based on overall traffic and transfer passenger growth and additional capacity on existing routes or new route growth. By 2018, Dublin was one of the fastest-growing airports in Europe; connectivity had increased by 59%, carrier numbers had doubled, and passenger numbers had risen by 45% to over 31 million.
Brisbane Airport. One of the fastest-growing in Australia in the last decade, leaned heavily on aero charge rebates and discounts to pursue Asian low-cost carriers. Within three years, its pre-pandemic Asian seat capacity expanded by more than 40%, and the number of airline partners more than doubled.
Differentiating for services
Increasingly, infrastructure charges are being separated from terminal charges to allow airports to offer a range of services such as buses, airbridges, electricity or preconditioned air to attract both full service and low-cost airlines. For instance:
Hong Kong Airport. The world’s eighth busiest airport pre-pandemic consolidated its multiple billing systems onto one platform, allowing it to deliver granular charging such as hand baggage limitations, parking utilities and overnight charging discounts.
Powering capacity optimisation
Beyond short to medium-term recovery, likely to be centred on peak periods, airports also need to ensure they can adapt to maximise the use of their current infrastructure. One way is by using behavioural incentive schemes. For example:
Dublin Airport. As a result of the rapid growth outlined above, operator DAA was experiencing bottlenecks at peak times. The charge structures were then keyed to encourage airlines to free up capacity in congested facilities, such as with discounts weighted on the significance of the capacity released. Other programs include significant runway charge discounts for long-haul morning arrivals using a remote stand, surcharges, and incremental time-based charge increases for long stayers or delayed stand departures.
Keflavik Airport. The airport operator Isavia frequently uses incentive schemes to smooth peaks and relieve congestion, both during the day and across winter/ summer.
Encouraging more sustainability
Many airports are now encouraging airline customers to use new, quieter, more environmentally friendly aircraft by adjusting charges to airlines based on environmental criteria. A study commissioned by the European Commission found that although 61% of European airports have already applied some charging levels for noise, currently, only 20% do so for emissions.
Swedavia. Swedish airport operator Swedavia is introducing a CO2 and NOx emission charge, following a government requirement that airport charges be differentiated for environmental purposes. Aircraft which emit more than average pay a penalty which finances a bonus for cleaner aircraft, with an overall airport revenue-neutral effect.
London Luton. Luton Airport has some of the most stringent noise control measures of any UK airport, building noise levels into its fees to dissuade carriers from using older, noisier aircraft.
A revenue management reset
Mastering the complex art of charge management within a regulatory cost framework – and invoicing accurately – isn’t always easy.
Some airports can fall into the black box trap – when commercial teams don’t have complete visibility into the impact of their schemes nor understand the back-office billing consequences.
For others, it’s the slow-to-cash malaise. Invoicing is delayed due to high-touch billing data preparation needs. Unbilled charges pile up through a lack of integrated operational data or overcomplex billing data preparation. Inaccurately applied discounts, particularly where multiple schemes are in play or multiple threshold criteria are required, leads to high levels of adjustments and credits.
To make sure that they can get the most from pricing signals and optimise their financial outcomes, airport operators need to ask themselves:
Does our commercial team have a clear picture of projected revenue and any operational impact of changes to the charge regime?
Are the native capabilities of aeronautical billing systems transparent enough that commercial teams can draw up winning schemes that are easy to implement in-house? Are there out-of-the-box capabilities to support standardised emissions-based charging, for example?
How much of the billing cycle can we automate to streamline the movement to the cash cycle and accelerate time to revenue?
Sharpening focus on flexibility and responsiveness
Airports that are the most flexible in using price signals are in the strongest position to encourage emissions reduction, optimise capacity and grow traffic. They are also the most able to adapt if regulation, commercial priorities or market dynamics change quickly.
Veovo fully supports ACI’s call to reconsider the use of strictly cost-based airport charges and heavy-handed regulation. In the meantime, airports must act quickly and decisively now. Where airports can pull levers to support their commercial and infrastructure needs, specialised aeronautical revenue tools can help airports inject flexibility into pricing models while remaining within regulatory boundaries.
Airlines have concerns and incorrect assumptions about the role of AI in powering the future of pricing. In this blog, we share insights from airline questions to Datalex regarding the role of AI in transforming airline pricing for the better, and for better revenues.
True AI-powered pricing, that eliminates rules and automated tasks, is a relatively new concept and is not fully understood industry-wide. Conor O’Sullivan, Chief Product Officer at Datalex and Navin Gupta, Product Manager for Pricing AI were on hand to answer burning questions, demystify some of the myths and addressing concerns that airlines have about AI-powered pricing.
Myth #1 – AI-powered pricing will replace my revenue management team
While some industry providers claim they will replace revenue management functions entirely, Datalex knows this is not what the industry needs. With Datalex Pricing AI, instead of replacing, we work collaboratively with airline revenue management teams to enhance what they do today, except in real-time and at scale.
Myth #2 – The airline will lose all control of its pricing strategy
We know from Datalex’s own research with airline executives that 93% of airlines say it is important to retain an element of control with AI-powered pricing so that the pricing strategy supports strategic objectives such as those to enter new markets, remain competitive, ensure customer loyalty, gain new customers, and maintain brand reputation. It’s important that an AI-pricing product is transparent the airline is satisfied that a degree of control is retained through supervising and monitoring techniques that give airlines peace of mind that they are very much still in control of their pricing strategy.
Myth #3 – Our airline will run into privacy / consumer rights issues with personalised pricing
This is a common misconception with AI-powered pricing and this concern is assuaged by the fact that we do not incorporate customer willingness to pay in order to sell the same product at different prices. The aim should be not be ‘personalised pricing’ in the strictest meaning of this; this aim should be ‘optimal pricing’ for optimal conversion dependent on demand and other inputs. This is the approach we have taken with Datalex Pricing AI, which is based on the concept of efficiency, in that the customer benefits from the optimal price based on market conditions at any given moment and this is beneficial to the airline because it is the most efficient price. For this reason, instead of mistakenly believing that AI-powered pricing is a customer-specific endeavour, it should be thought of as customer-centric as it is the most efficient price for any customer at any given moment. AI-powered pricing should be customer-centric, not customer specific.
Myth #4 – Our airline can make the best pricing decisions quick enough because we know our business and our customers
What your airline does today is most likely a limiting price decision-making process that tracks demand and does not take real-time competitor pricing and customer segmentation into account. Once revenue management teams consider and deal with each input and constraint, they must immediately start all over again. Thus, creating a trade-off between accuracy and speed which is a vicious circle with current revenue management processes, and a significant hindrance to an airline’s ability to react. A revenue management team cannot process all relevant demand factors, competitive insights and suggest the most optimal price at every given moment. Some products, like Datalex’s Pricing AI product, can process all inputs at any moment – within 200 to 500 milliseconds to be exact.
Myth #5 – Our airline data is exposed, we will be exposing our data to other airlines
It’s a common misconception that AI is a generic tool used across customers, but powerful, intelligent AI tools are uniquely trained and completely bespoke to each airline because it leverages an airline’s proprietary data and no other.
Myth #6 – We don’t have the internal resources needed to integrate with our revenue management system / We don’t know or have the right AI expertise
Internal resources and a lack of AI expertise are cited by airlines as obstacles to overcome in implementing AI-powered pricing. What is different about AI is that it is built on scalable, easy-to-integrate technology that is a huge change from existing legacy systems. Despite the technology being SaaS based, AI-powered pricing products still integrate seamlessly with existing legacy systems and an airline’s existing Revenue Management tools and processes.
AI education and awareness must not be underestimated. It’s important for airlines to trust technology and experienced technology providers like Datalex to fully embrace the potential of AI and lean on AI experts in the process to bring them along on this journey.
Myth #7 – Our revenue management team will not learn anything if they hand all the data over
It is important for Revenue Management to feel close to and involved in the AI process. To this end, we at Datalex work closely an airline’s revenue management team directly when models are trained initially. Decisions made by models which are reviewed with Revenue Management teams to assure them that it works as expected. We do not take a “big bang” approach and we always advise deploying the product initially into a small number of markets. AI adoption is a journey.
Beyond the obvious revenue tailwind that AI-powered pricing represents (which has been proven to increase revenue by 2% – 3.82% in Datalex production trials), there are many more opportunities for airlines to stay competitive in-market.
With demand patterns ebbing and flowing, customer confidence changing like the wind, geo-political issues and staff shortages – all industry-wide problems – airlines must have a dynamic, fast acting product that they trust is ready to react at any given moment to such a an ever-changing travel landscape. environment.
Many airlines are keen to start their AI-powered pricing initiatives, but it must be seen as a journey that is taken in steps, with existing revenue management teams in control every step of the way.
Conor O’Sullivan, Chief Product Officer at Datalex Navin Gupta, Product Manager for Pricing AI