In the U.S. alone, over 2 million customers book travel every day . For each of these trips, there are individual motivations, wants, needs, and willingness to pay that determine when, how, and with whom these customers book their travel. Suppliers of travel, such as airlines and hotels, grapple with the difficulties of providing meaningful travel services to satisfy individual preferences, while also reaching the scale that allows for profitability. Machine learning can offer a solution to this dilemma, and JetBlue has taken steps toward incorporating machine learning into their travel product development.
Ever since the U.S. Government deregulated air fares in 1978, airlines have struggled to effectively match the services they provide with customer demand. In 1978, every customer was provided the same level of service (including “ancillary” products such as checked bags, meals, seat assignment, ability to cancel or refund, etc.) regardless if the individual passengers needed or wanted these services. Today, many airlines, including JetBlue, offer several different tiers of service for passengers with different “ancillary” products bundled together, while others such as Spirit employ a fully a-la-carte model. This trend toward individualization is continuing; in 2017, revenue from the sales of ancillary products made up 10.6% of total airline revenues, compared to 4.8% just eight years earlier . In order to continue to offer further individualized product offerings, JetBlue’s management will need to gather real-time insights from ever increasing sources of data on existing and potential customers to offer personalized travel products to match each individual’s preferences and needs.
JetBlue’s management has taken steps to address this changing landscape of airline product offerings in the near term by partnering with FLYR Labs, a technology start-up focusing on enabling real-time dynamic pricing for the aviation industry . FLYR’s technology utilizes machine learning to analyze incoming data streams from a potential customer (demographic information, past purchase behavior, online presence, etc.) and determine the optimal offering of air ticket, ancillary product, hotel, rental car, etc. to offer to that customer . For example, JetBlue’s product bundling today looks like Exhibit 1 :
There are four fare products (Blue, Blue Plus, Blue Flex, Mint) that each offer a specific combination of ancillary products for a given price point. In the future, with machine learning enabled algorithms, a search result for air travel may look something like Exhibit 2 :
JetBlue can predict what ancillary services will be needed by each individual consumer, and can serve a personalized offering to them.
In the medium term, JetBlue has taken one main step toward the development of continued organizational improvement in machine learning, the creation of JetBlue Technology Ventures (JBTV), a corporate venture capital fund. JBTV is currently the only travel provider corporate venture firm, and this position gives them access to thousands of start-ups that are looking to innovate with new technologies such as machine learning. According to Bonny Simi, President of JetBlue Technology Ventures, the venture fund allows JetBlue to “look around the corner and see what’s coming so we can adapt our business model as a large company to those shifts and also be a first mover in some of the great technologies that are coming forward” . As new companies seek to use machine learning to enhance the aviation industry, JetBlue is ideally positioned to discover and invest in the most promising ventures.
Beyond the current steps that JetBlue’s management has taken to implement machine learning into their product development, I believe that there is opportunity to expand machine learning techniques beyond product development, and into process improvement. One example of a potential process improvement that JetBlue can explore is in the realm of passenger connections in their hub airports. With current technology, it is very difficult to track connecting passengers once they de-plane their first flight and before they board their next flight. Research has shown that machine learning algorithms are more accurate at forecasting passenger connecting times than current benchmarks and tools . JetBlue can apply these techniques to predict which customers on specific arriving flights are most at risk of missing their onward connection, and then intervene to assist that passenger in making their onward flight and avoiding a service disruption.
One open question on the topic of machine learning for product development in the context of JetBlue is what challenges implementation will bring. The path from the current state to the future state described above is unclear, particularly due to the competitive environment in the travel industry. Although machine learning can in theory provide better value to both consumers and travel providers by more efficiently matching service levels to needs, the infrastructure and existing distribution channels can not support this kind of selling. How can JetBlue move forward towards more individualized product offerings without the rest of the ecosystem rejecting their efforts? (797 words)
 2017 Bureau of Transportation Statistics, T-100 Market (All Carriers)
 2018 CarTrawler Yearbook of Ancillary Revenue
 Wall Street Journal, “JetBlue Tech Execs Tap Startups To Help Airline Innovate”, July 13th, 2018
 www.jetblue.com , search results for BOS-LAX itinerary, accessed November 12th, 2018
 Created by Author
 Phocuswire.com interview, “In The Big Chair – Bonny Simi of JetBlue Technology Ventures”, August 22, 2018
 Forecasting Airport Transfer Passenger Flow using Real-time Data and Machine Learning, Guo et. al. HBS Working Paper 19-040