Data Science in Travel and Transportation

Today, travel companies use big data to create a 360-degree view of each customer. In lieu of generic recommendations, companies are now within reach of the possibility of providing .

This view in the round can be assembled from a number of datasets:

Some of these datasets (e.g., behavioral targeting) will be more relevant for new customers; others (e.g., previous purchase patterns) will come into play when companies are looking for ways to cross-sell and up-sell.

The goal is to create the right offer through the right channel at the right time and place.

The era of personalization is creating another benefit. It’s giving the transportation industry a chance to improve its customer service.

Delta Airlines is providing their flight attendants with a Guest Service Tool to use on their SkyPro devices. Attendants use this device and tool to enhance the customer experience by reviewing preferences.

Similarly, United Airlines attendants have a tool that provides them with information on customers such as their last flight, dietary needs, and connection schedule.

Naturally, some customers are going to travel more than others. That means companies need to be acutely aware of the big players so they can avoid customer churn.

The travel industry already has a giant legacy of data on MVCs from loyalty programs. The trick is combining this historical information with real-time and predictive analytics to anticipate what MVCs will likely want in the future. It costs far more to acquire a new customer than it does to retain an old one.

Let’s say you’re traveling to Buenos Aires for a four-day business conference and you’ve decided to take a weekend to explore the city. You’re looking for a flight that leaves Sunday and returns early Monday.

If your airline has invested in big data, it’s going to be offering you cross-selling and up-selling opportunities from the moment you begin your search.

You may:

When companies have the resources to partner with others (e.g., airlines with airports, train and taxi services), cross-selling opportunities can skyrocket.

In the world of travel, complete customer behavior profiles + increased collaboration = more revenue generation.

When it comes to safety, big data can be a literal lifesaver. Today’s planes, trains and automobiles are equipped with a wide array of sensors. These provide control centers with a continuous stream of real-time data on every aspect of the journey (e.g., airmanship or driver behavior, environment, mechanical performance, etc.).

With this information in hand, transportation data scientists are creating complex algorithms to predict problems and, even better, prevent them.

Not only do these steps slash costs and increase reliability – imperative in a 24/7 industry like travel and transportation – they help avoid catastrophic accidents.

In addition to sensors, data scientists are extracting information from all corners of the digital universe to streamline day-to-day operations.

For example, companies are:

Automation has been a massive help in this effort:

In the future, even your trip bookings may be automated. With only a fuzzy idea in its search engine as a start, the travel start-up Hopper is aiming to employ big data algorithms to plan and book your personalized trip. No effort required.

Like many other industries, travel and transportation has big-data challenges. The veracity of data is in question. Technology changes by the hour. And we haven’t even started on:

That’s not all. The airline industry has another problem…

Ironically, airlines have become victims of their early success. Early investment in big-data technology has left them with clunky and aging operational systems. Some are wrestling with a legacy of reservation software that’s fifty years old.

Start-ups and online travel firms have the luxury of creating centralized data warehouses from scratch. Older companies like airlines typically find ways to come up with a hybrid environment where big-data technologies can co-exist with existing hardware and legacy software and databases. It’s not going to be easy.

Finally, here’s a cautionary tale from the big data front. In 2012, news sources reported that Orbitz was offering costlier featured deals to Mac users. The reason? Their big-data algorithms had determined that Mac users spend up to 30 percent more on hotels than Window users.

Though all visitors were able to see the same hotel pricing options in search, some Apple customers weren’t exactly thrilled with Orbitz’s predictive analytics coup.

And this wasn’t even close to 360-degree profiling. As companies continue their drive towards data acquisition, questions on individual privacy will continue to arise:

Travel companies, which often store sensitive credit information, must also be vigilant about digital safety and security measures. The more data they collect on individual passengers, the more attractive they become to cyber-criminals.

“You put together the best team that you can with the players you’ve got, and replace those who aren’t good enough.” – Robert Crandall

First there were horse-drawn carriages. Then there were trains. Then there were planes. At each step along the evolution of transportation, there was data – reservations, arrivals and departures, cargo capacities – and savvy entrepreneurs, to make the most of it all.

And it is with C.R. Smith and American Airlines that we start our story of computerized data analysis.

In the 1940s, American Airlines had a problem. Despite advances in technology during World War II, airline employees were still encumbered with a manual booking process. Finding a seat for a customer, updating the inventory and recording passenger data was a timely process.

Fortunately, American had hired engineer and data expert Charles Amman to work on a solution. In 1944, he mocked up an automated system for storing seat inventory and showed his idea to Smith. Approved, the idea was then implemented by the Teleregister Company of Stamford, Connecticut.

Together, they built the Reservisor, an electromechanical flight board that could broadcast seat availability. It first went live in February 1946.

There was one weak point left in the Reservisor system. It still depended on manual input from multiple employees. Booking a flight – in an era where passenger volume was exploding – could take as long as three hours.

Trans-Canada Airlines (TCA) had developed a system, ReserVec, in 1953 that was working much more efficiently, but it couldn’t break into the U.S. market.

Time for a little serendipity. That same year, on a flight from Los Angeles to New York, C.R. Smith met Blair Smith, an IBM sales executive (they bonded over names) and asked him if IBM’s computer experts couldn’t do something to help.

The result? IBM and American developed the Semi-Automatic Business Research Environment (SABRE). Sabre was built on teleprinter technology initially developed for the U.S. Air Force. With IBM’s electronic brain in place, American’s reservation process became a thing of automated beauty.

Fully operational in 1964, Sabre was the largest private, real-time data processing system in the world. It was quickly joined by Deltamatic, PANAMAC, Apollo and PARS.

Not long afterward, airlines started experimenting with the idea of loyalty programs. The Airline Deregulation Act of 1978 was creating cutthroat competition. Companies needed to stand out.

AAdvantage was a case of big data in action. Data analysts combed Sabre’s computer reservation system for recurring phone numbers to detect frequent flyers. These folks were pre-enrolled in the program and offered a variety of benefits – complimentary upgrades, free or discounted tickets, etc.

Loyalty programs later expanded to include hotels, rental car agencies and other carriers. Airlines were now swimming in data about their customers’ travel habits.

Meanwhile, as the seventies slid into the eighties…

In 1987, European airlines including Air France and Lufthansa developed Amadeus, a global distribution system that could compete with Sabre.

Deregulated fare and schedule structures, a shift to a hub and spoke strategy (with more connecting flights) and increased competition from new low cost carriers like People Express Airlines had another effect in the 80s.

It prompted Robert Crandall of American to set up the first automated yield management system, and many more to follow him.

A yield management system helps airlines provide variable pricing for a fixed resource (e.g., seats) in order to maximize their revenue. In the best-case scenario, this analytics-based inventory control system is capable of:

It can also estimate demand and cancellation rates.

Though it took some time to develop, American’s Dynamic Inventory and Maintenance Optimizer (DINAMO) was fully implemented in 1988. It allowed American to undercut the competition and increase productivity per analyst by 30 percent in two years.

The rumblings had been happening for some time. As far back as 1985, Sabre had launched easySabre®, which provided online access to bookings for airlines and hotels.

But it was in the 1990s that worldwide access to data truly exploded. In 1991, UPS deployed its handheld Delivery Information Acquisition Device (DIAD), delivering information back to headquarters and marking a milestone in strategic use of data. In 1995, customers gained the ability to track their packages in transit via UPS’s website.

In 1996 alone:

Airlines were well aware of the changes. In 1999, they partnered to develop industry standards for the electronic exchange of business information. Two years later, in 2001, five of the six major carriers launched Orbitz.

As search technologies grew in sophistication, so too did websites. In 2004, cofounders of Orbitz, Expedia and Travelocity unveiled Kayak.com. Kayak’s “metasearch” model gave users a central place to search for and compare airline prices across multiple sites.

Last updated: June 2020