In the first blog post in our series on the history of revenue optimization, 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 pricing for airfares, hotel bookings, and other travel-related services.
In the second article in this series, A Brief History of Revenue Optimization: Airline Fares and Price/Time-to-Flight Curves, we examined one key aspect of airline pricing strategies: 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.
In this article, we’ll take a closer look at the data historically needed for successful revenue optimization efforts and some of the challenges and advancements that have beset data-focused revenue optimization efforts. When Robert Crandall developed his early revenue optimization techniques — which he called yield management — the idea was to maximize revenue by managing the inventory of airline seats based on demand forecasting.
From the beginning, successful revenue optimization efforts depended on careful data analysis. And data analysis relies foremost on collecting useful data (comprehensive and correct data sets). Many businesses collect data but aren’t particularly worried about noise or the occasional incorrect value. That’s a huge problem in gaming. If you’re not validating the data you collect, are you really collecting data?
Computerized Booking and Data Capture
Revenue optimization in the travel and hospitality industries, including airlines and hotels, relies heavily on rich, long-term data sources to enhance decision-making processes. These data sources help organizations develop strategies that maximize revenue by predicting demand, optimizing pricing, and managing inventory efficiently.
Airline revenue optimization efforts began in the 70s and heavily leveraged the emerging development of computerized central reservation systems (CRS) that had started becoming available in the mid-1960s. American Airlines and IBM had collaborated on the first widely successful CRS, the Semi-Automatic Business Research Environment (SABRE), which went online in 1960. Other airlines followed suit, leading to a number of competing CRS systems, including Delta’s DATAS, TWA’s PARS, and United Airlines’ Apollo.
For context, many of the modern data capture, storage, and analysis tools we’ve come to appreciate were not yet ready for prime time or even available at all. Prior to the early 1970s, reservation systems relied on manual record keeping and tabulation, microfilm, or flat data files. The relational data model paper by Edgar Codd was published in 1970. SQL (then SEQUEL) was initially developed at IBM during the mid-1970s. And commercial relational database management systems (RDBMS) weren’t available until 1979.
When Ken Littlewood wrote his 1972 paper Forecasting and Control of Passenger Bookings, BOAC’s BOADICEA system was a key element. It provided accurate, historical booking data for analysis and prediction that could later be correlated against actual flight manifest data.
The BOADICEA system’s Flight Transaction History File is a good example of the data available for early airline revenue optimization analysis. The Flight Transaction History File represents a historical compilation of passenger name records (PNRs) extracts dumped from daily flight departure reports.
“Each line represents a transaction associated with the flight and shows the number of days before departure and the time (in minutes past midnight) at which the transaction was made. The sector booked, the class and the number of seats sold are also recorded.”
Codes provided further metadata about a booking, such as cancellations, no-shows, upgrades or downgrades, and departed loads by sector and class.
The features available for analysis (flight, number of days, time, sector, class, number of seats sold, price) were chosen based on subject matter expertise. There were a very limited number of attributes used to build models because of computational restrictions. In the modern world, we can handle hundreds or thousands of covariates, and we have much more advanced statistical techniques available to help us choose and evaluate them.
The data was rudimentary, but it was enough for Littlewood to perform critical analysis, such as forecasting demand by day and sector and comparing the forecasted demand to actual customers in seats at flight time. While most airline optimization research was based on maximizing the number of passengers per flight, Littlewood was able to use the limited data on hand to also generate a successful model for maximizing revenue by controlling low-yield fare allocation versus high-yield fares.
As similar digital booking capabilities and data storage and analysis tools became increasingly available across the travel industry, more sophisticated revenue analysis and optimization efforts usually followed.
Bill Marriott Jr.’s work to formalize a practice in the hotel business of ensuring high occupancy while maximizing revenue per available room (RevPAR) coincided with the emergence of digital property management systems (PMS) such as Springer-Miller’s now ubiquitous SMS Host and SABRE expanding access to hotels and rental car agencies in the 1980s.
Capturing More Detailed Data About Bookings and Customers
Historical booking data has been at the core of airline and travel-related revenue optimization since the very beginning. This data includes records of previous reservations, which provide valuable insights into overall demand patterns based on several factors:
Booking rates provided high-level information about how flights or rooms were booked during specific periods. This helps airlines and hotels forecast future demand. No-show and cancellation rates monitored trends related to customers who cancel or fail to show up. This provides critical data to adjust inventory, overbooking strategies, and pricing.
Flight patterns identified popular routes and peak travel times. This information allows airlines to optimize seat pricing and availability for high-demand flights. In the broader travel industry, hotels also adopt this approach by capturing data on occupancy rates, length of stay, and lead times for bookings, allowing them to tailor room availability and pricing strategies based on patterns observed in past reservations.
Moving into the 1980s, the widespread availability of computerized reservation systems in both airlines and hotels marked a significant shift in data collection and analysis. These systems recorded every booking transaction in real time, enabling companies to store, track, and analyze large volumes of data. Reservation trends—tracking bookings across time—provided insight into periods of high and low demand, enabling better forecast accuracy. This is an advanced approach to early booking rates, aided by more sophisticated software and larger datasets to enable more detailed analysis of reservations.
The ability to capture more detailed data across time meant that customer histories and demographics became more extensive. Collecting passenger or guest information provided airlines and hotels with the ability to “segment” their customer base, identify key target groups, and tailor pricing and offers to specific audiences. The key insight leading to the early success of SMS Host was “to build a complete PMS around the guest history profile instead of the more typical rooms-based approach.”
As the airline industry shifted from direct phone sales and travel agent bookings to web-based bookings, the ability to collect even more direct data about customers exploded. Analyzing online search data, booking times, and the decision-making process allows companies to identify the factors influencing booking decisions and adjust their pricing accordingly. Understanding how users navigate an airline’s or hotel’s website enables optimization of the booking funnel, ensuring that customers are offered the right product at the right price and increasing conversion rates.
Today, airlines capture many orders of magnitude more data about bookings and customer preferences than Littlewood had available in 1972. The standardized IATA Airline Industry Data Model (AIDM) used across the industry contains over 60 fields just for its Passenger Bookins dataset and over 40 fields for route-specific data. Each airline keeps additional proprietary datasets of booking, loyalty program, and demographic data that it can use for analysis and optimization.
While the details of these datasets may be closely kept, it’s possible to extrapolate the details captured from products offered by third-party data brokers. For example, Techsalerator’s Airline Passenger Data Products is “primarily collected from airlines and their associated systems, including reservation systems, passenger service systems (PSS), and customer relationship management (CRM) platforms. These systems capture data during the flight booking process, check-in procedures, boarding, and other interactions with passengers.” Their People Lifestyle and Behavior Data alone contains over 100 fields of data about individual customer lifestyle and purchase details.
The War for Customer Data
Direct online sales provided improved access to customers and data but came with some new challenges. The rise of online fare aggregation sites such as Expedia, Kayak, and Skyscanner significantly impacted the way airlines and hotels collect data about customer bookings. These platforms act as intermediaries, allowing consumers to compare prices across various providers and make bookings without directly interacting with the airline or hotel’s booking system. As a result, airlines and hotels lose access to valuable data about booking behavior, preferences, and demographics.
To counter this shift, airlines and hotels have increasingly turned to loyalty programs as a way to regain direct relationships with customers. Frequent flyer programs and hotel rewards memberships incentivize customers to book directly with the company by offering points, discounts, and exclusive perks. Through these programs, airlines and hotels regain the ability to capture direct, detailed information about their members, including travel patterns, preferences, and frequency of bookings. This first-party data is crucial for enhancing revenue management strategies and delivering a more tailored customer experience.
Airlines leverage their loyalty programs, websites, and branded credit cards as powerful tools to capture detailed customer data. Websites and apps collect browsing behavior, booking preferences, and spending patterns over time, as well as responses to advertisements and personalized offers. Branded credit cards add another layer, offering insights into non-flight purchases such as dining and retail spending. This rich dataset feeds into advanced data models, enabling the prediction of customer preferences and fine-tuned marketing strategies.
Instead of sending travelers to a hotel website or third-party service, airlines position themselves as one-stop travel hubs. They can cross-sell or bundle these services with flights, creating seamless booking experiences that further increase customer loyalty. And rather than handing off both booking revenue and customer data to Expedia, Kayak, or Skyscanner, the airline itself captures both a higher share of the traveler’s overall spending and a wider data model of overall travel preferences from which it can further tailor future offers and optimizations.
Data Privacy Concerns for the Travel Industry
The growing reliance on data for revenue optimization in the travel industry has brought with it significant concerns regarding data privacy. The collection and use of large amounts of customer data, which fuels modern pricing, inventory management, and demand forecasting models, must now be balanced with the responsibility to protect consumer information. Several key data privacy issues have directly impacted the data available for revenue optimization efforts in the travel industry.
One of the most significant impacts on data collection and usage practices has been the introduction of stringent data privacy regulations globally. These regulations aim to safeguard consumer privacy and ensure that companies handle personal data responsibly. Major regulations include General Data Protection Regulation (GDPR), GDPR is one of the most comprehensive data privacy regulations. It mandates strict guidelines on how personal data is collected, processed, and stored.
For airlines and hotels operating in or with customers from the EU, GDPR compliance means informed consent, the right to be forgotten, and data minimization.
Detailed data about customers’ search and booking behavior, travel preferences, and loyalty program participation are valuable for segmentation and personalization. However, if customers choose not to share this data or if they opt out of tracking mechanisms like cookies, airlines and hotels lose access to this important revenue optimization input.
Historically, airlines, hotels, and third-party vendors such as travel agencies have shared customer data to improve services and optimize pricing. Data privacy regulations, customer concerns, and competitive forces now impose limitations on these practices. For example, GDPR’s data-sharing rules require companies to justify the need for shared data explicitly and to secure explicit consent from customers, which complicates data flows and limits collaborative revenue optimization efforts.
To learn more about the privacy and compliance issues facing all industries using customer data, see the previous articles by Greg Doane, Privacy Laws Move Forward. Have You Kept Up? and What is Compliance in Games?
Diversifying the Product Line
The traditional model of airline revenue optimization has long revolved around maximizing yield on different seat categories. Airlines would predict demand for various seat classes, then allocate seats accordingly, pricing them to maximize revenue. This approach relied on increasingly larger datasets of historical data and demographic insights to determine how many seats of each category should be offered and at what price point to achieve the optimal balance between capacity and demand.
The modern revenue optimization model has evolved significantly, expanding beyond simply pricing airline seats. Today, airlines also offer a broad range of unbundled additional services and products to generate revenue. When booking through an airline’s website, customers are now presented with an integrated experience that includes hotel accommodations, car rentals, and even restaurant reservations. This diversification of the product line allows airlines to generate additional streams of revenue by packaging travel-related services alongside the flight itself, thus creating a one-stop shop for travelers.
In addition to these ancillary services, the concept of what constitutes a product for sale during the flight itself has transformed dramatically. Rather than viewing the airline seat as a single purchase, airlines have unbundled aspects of the individual flight experience into distinct, purchasable items. For instance, customers can now opt to buy meals or checked luggage separately. Seats themselves have become more varied, with airlines offering different options such as extra legroom or “premium economy,” priced according to demand. Other services, such as in-flight Wi-Fi, entertainment, or priority boarding, are also priced independently, allowing airlines to offer specific pricing for each feature based on customer preferences and market demand.
This shift in product offering is closely related to the “Free to Play” model seen in the gaming industry, where the core experience is available at a base price (or even for free), but additional features and enhancements can be purchased à la carte. In the airline industry, this approach allows for greater flexibility and personalization. Customers can pick and choose which elements of the travel experience they wish to pay for, and airlines can adjust pricing for each service dynamically based on real-time demand. By re-bundling options into personalized offers—such as flight and hotel packages or discounted car rentals with flight bookings—airlines can appeal to a broader range of customer preferences while encouraging customers to make higher-value purchases.
The evolution from the basic, seat-based pricing models of the 1980s to today’s highly diversified and complex pricing strategies is remarkable. Where airlines once operated with a limited set of parameters for determining seat prices, the modern approach involves hundreds of factors that influence both seat prices and the pricing of a wide array of ancillary services. Advances in technology, big data, and real-time analytics allow airlines to fine-tune their offerings to meet the precise needs of their customers, capturing more value from each flight than ever before.
Ready for Takeoff
Revenue optimization in the airline and travel industries has evolved from relying heavily on limited historical data and basic forecasting models to employing sophisticated real-time data, external market information, and advanced machine learning algorithms. The increasing ability to leverage diverse data sources, including booking data, competitive information, and customer behavior analytics, lets companies make informed, dynamic decisions that optimize pricing and maximize revenue. As technology continues to advance, the role of data in revenue management will only grow, providing even more precise and effective optimization strategies.
Improvements in algorithms and changes to the product mix are the other components of a revenue optimization strategy. Revenue optimization in the airline and travel industries has evolved from relying heavily on limited historical data and basic forecasting models to employing sophisticated real-time data, external market information, unbundled products, and advanced machine learning algorithms (In particular, while I haven’t focused on algorithms in this article, but papers like Dynamic Pricing for Airline Ancillaries with Customer Context are fascinating.)
At every step along the timeline of revenue optimization, new technologies have enabled businesses to collect more detailed data and create more sophisticated pricing and inventory management models. Moving from manual record keeping to CRS and PMS applications allowed airlines and hotels to track bookings and cancellations, providing foundational data for early forecasting models. Over time, advancements in data storage and analytics made it possible to analyze larger datasets and incorporate external factors, such as competitor pricing and market demand, into revenue management decisions. From the first relational databases to today’s cloud-based data lakes, more information enables businesses to personalize offerings and maximize profits with unprecedented accuracy.
In future posts, we’ll continue exploring the history of revenue optimization in games, focusing 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.
