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As long as outdated systems and obsolete methodologies remain in place, airlines will continue to fall behind other industries in maximizing revenue potential.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. FLYR’s decision intelligence platform, The Revenue Operating System^®, embraces advanced deep learning technology to provide automated, AI-driven revenue management capabilities that maximize airline profitability and total revenue optimization.

The success of this approach is behind FLYR’s hyper-focus on context. Our deep learning technology is able to map each individual piece of commercial data or input to all others to provide the context behind the data point and make predictions or identify when a customer is likely to convert at the existing price, and what is the optimal product bundle for each traveler, adjusting in real-time without the airline analyst having to wait for the next filing.

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.

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