In the competitive airline and travel industries, revenue optimization is critical to maximizing profitability. This strategy, commonly referred to as Revenue Management (RM), uses advanced analytics to predict consumer behavior, optimize product availability, and adjust prices to the ideal levels at the right times.
The concepts of dynamic pricing and revenue optimization are not limited to the airline and travel industries. They are also crucial to industries including gaming and digital entertainment. Just as airlines adjust prices based on demand and timing, game developers can optimize pricing for digital content and in-game purchases by understanding consumer behavior and market trends.
In a previous blog post, The Origins of Revenue Optimization, we explored the early practices of revenue optimization in the airline industry, where dynamic pricing and sophisticated forecasting models revolutionized how tickets were priced and marketed. Other travel-related industries have since adopted this approach to optimizing revenue. The evolution from simple pricing strategies to complex algorithms that consider a multitude of variables, such as consumer behavior and competition, is now used across multiple sectors.
In this article, we’ll examine one key aspect of the strategy used by airlines for pricing fares: price/time-to-flight curves map changes in ticket prices as the departure date nears. These curves demonstrate a simplified version of the algorithms that airlines use to strategically price tickets to maximize revenue while filling as many seats as possible.
Understanding Price/Time-to-Flight Curves
In the context of airline ticket sales, price optimization involves adjusting the price of tickets over time to maximize revenue. Price/time-to-flight curves are graphical representations that illustrate how the price of an airline ticket changes as the departure date approaches. Here’s a broad-brush breakdown of what these curves represent—frequent fliers and expert fare chasers will know that, in practice, there is more complication to actual airline seat categorization and pricing practices.
The early booking period typically presents initially low prices. Right after the tickets are released, prices are usually low because airlines target price-sensitive customers who plan their trips well in advance to secure their seats at the lowest expected price.
The mid-booking period adopts price stabilization. During this time, prices fluctuate moderately as airlines gather data on demand and adjust prices accordingly. If demand is lower than expected, airlines might keep prices lower to stimulate sales. Conversely, if demand is high, prices may increase.
The late booking period features gradual price increases. As the flight date approaches, prices generally increase as available seats become more limited. Airlines capitalize on last-minute travelers who are often willing to pay higher prices for convenience or out of necessity or business travelers who are generally less price sensitive. Occasionally, if there are many unsold seats, airlines might offer last-minute discounts to fill the plane, resulting in a brief dip in prices before the final surge.
Prices are frequently at their highest on the day of departure, which targets travelers who need to book a flight urgently.
Factors influencing the curve include demand forecasting, competition, seasonality, and events. Airlines use historical data and current booking trends to forecast demand and adjust prices. Prices can be influenced by the pricing strategies of competing airlines on the same route. Prices may vary significantly depending on the season (holidays and prime vacation months) and specific events (sports events or conferences).
However, while these examples cover the general case, each flight’s fare pricing over time may move in different directions due to dynamic attributes of the prior bookings, connecting airports, time of year, and other factors.
Adding Some Real-World Complexity to Dynamic Fare Pricing
So far, we’ve just looked at a generalized picture of price/time-to-flight curves. In the real world, dynamic pricing for airline tickets involves numerous data points and factors that help airlines maximize revenue while efficiently managing seat availability. Key elements influencing pricing strategy and price/time-to-flight curves include the airline’s internal seat or fare classes, frequent flier programs, competitor pricing, customer segmentation, overall economic conditions, and real-time data analytics.
Seat or fare classes are categorized into different types, such as economy, premium economy, business, and first class, each with its own pricing strategy based on demand, amenities, and target customers. Airlines dynamically manage the number of seats available in each fare class, increasing or decreasing availability based on booking patterns. Different fare classes have varying levels of flexibility, with more expensive classes offering benefits like refunds or changes, leading to price differentiation within these classes.
Frequent flier programs incentivize loyalty by offering points or miles that can be redeemed for flights, upgrades, or other benefits. Airlines set aside a certain number of seats for redemption with frequent flier miles, and the availability and points required can change dynamically. Additionally, frequent fliers often have access to exclusive discounts or early booking windows, which can affect overall pricing strategies.
Understanding when different customer segments book flights is crucial for tailoring pricing strategies. Leisure travelers often book in advance and are price-sensitive, while business travelers book closer to the departure date and are less price-sensitive. This results in different price adjustments over time. Airlines analyze past booking data to forecast demand and adjust prices accordingly, using machine learning models to predict booking curves and optimize pricing.
This comprehensive approach highlights the airline industry’s complexity and sophistication of modern revenue management strategies. By integrating these factors into their dynamic pricing models, airlines can optimize their revenue while efficiently managing seat availability and responding to market changes.
An Example of a Price/Time-to-Flight Curve
A price/time-to-flight curve graphs a predicted or actual airfare over time, where the x-axis represents the time to flight (days, weeks, months) and the y-axis represents the price.
Based on our theoretical model for airfare pricing so far, consider the following possible pricing curve for seats on flight #123, where the price is reflected on the Y-axis against time-until-flight on the X-axis:
Is this correct? The answer is, of course, “it depends.” To truly understand the proper pricing curve shape, you’d need to consider various factors:
- Historical Fill Rates: Does this flight tend to fill up historically? Understanding past trends helps set a baseline for expectations. If the flight typically sells out, higher prices can be sustained longer.
- Booking Patterns: When do travelers usually book their seats? If data shows that most bookings occur well in advance, the pricing strategy might involve higher early prices with occasional discounts to stimulate early sales, followed by a steady increase as the flight date approaches.
- Priors and Trust: How reliable are these historical patterns? Do we trust these priors, or is there significant variability? The pricing strategy can be more confident if the historical data is consistent. If not, more dynamic adjustments may be needed.
- Deviations and Covariates: For times when historic booking curves deviated from the “norm” (whatever that is), what covariates can be identified that correlate to those deviations, and which of these are “in play” with regard to the current flight bookings? Key covariates include:
- Time of Year: Seasonal variations can significantly impact demand. Holidays, school vacations, and other seasonal factors can cause spikes or drops in bookings.
- Events at the Origin or Destination: Local events, such as festivals, conferences, or major sports events, can drive up demand and allow for higher pricing.
- Alternative Destinations: If other destinations are more appealing due to events or promotions, demand for this flight might decrease, necessitating a different pricing approach.
- Day of the Week: Business travelers typically book more on weekdays, while leisure travelers might prefer weekends. Understanding this mix can help set optimal prices.
- Economic Factors: Broader economic trends, both global and local, impact consumers’ willingness to spend on travel. During economic downturns, more aggressive pricing might be needed to stimulate demand.
There are many others, some of which are nearly impossible to anticipate.
Understanding these curves helps airlines optimize their pricing strategy to maximize revenue while ensuring high seat occupancy. By continually analyzing and adapting to the factors mentioned, airlines can refine their dynamic pricing models to better match supply with demand, ultimately leading to a more profitable operation.
Real-world pricing tends to be much more variable, sometimes demonstrating price/time-to-flight curves much different than our theoretical model. Some of the frequent flier blogs provide examples of airfare time analysis that demonstrate the complexity of predicting dynamically priced fares. For example, godsavethepoints.com shows pricing for a flight to Hawaii that peaks just before day of flight, as predicted by our simple model.
However, The Points Guy shows an example of an airfare to San Francisco that dips lower than its starting price at several points and reaches its lowest price in the days just before the flight.
What can you take away from this? Revenue optimization is challenging and encompasses a wide range of variables and strategies.
Applying the Lesson of Price/Time-to-Flight Curves to Digital Entertainment
Airlines maximize their revenue by adjusting prices dynamically based on factors such as demand, seat availability, and booking patterns while efficiently managing seat occupancy. This method is relatively straightforward—it simply tries to adjust to the optimal overall revenue for seats on a flight, seats that expire once the flight pulls away from the gate.
In contrast, the revenue optimization challenges faced by the game and digital entertainment industries are much more complex. The revenue streams are more diverse and influenced by a wider range of variables, including user engagement, in-game purchases, subscription models, and content updates. Unlike airline ticket sales, where the product is a single, non-repeatable purchase, digital entertainment products require tailored strategies for multiple monetization points.
Even within airline fare price optimization, the price/time-to-flight curve represents only one part of a much more sophisticated system. Advanced revenue optimization for airlines incorporates machine learning algorithms, real-time data analysis, and complex forecasting models to adjust prices dynamically. Factors such as loyalty programs, ancillary revenues from baggage fees and onboard services, and strategic partnerships also play critical roles in the overall pricing strategy.
Revenue optimization for games and digital entertainment involves a significantly more complicated array of data inputs and considerations. These include user engagement metrics, audience profile segmentation, in-game purchase behaviors, subscription models, content updates, and a constantly evolving market landscape.
In future posts, we’ll examine the history and background of revenue optimization in games, focusing more on the complicated and shifting landscape in which game and digital entertainment publishers must operate and highlighting the unique challenges and opportunities in pursuing digital revenue optimization strategies.
