Couturier's observations stem from "Big Data in the Travel Industry," a report Amadeus just released with analysis provided by Tom Davenport, the noted analytics expert, author and college professor. The report finds that innovative approaches are flourishing, with a growing list of real-world examples of big data and advanced analytics in action:
British Airways is doing more to remember personal preferences with its Know Me program, which goes beyond the loyalty programs based strictly on mileage rewards. For example, it can spot when passengers choose window seats for short-haul flights and aisle seats for long-haul flights because they want to stretch their legs, and that pattern can be repeated automatically.
"They're combining everything they know about passengers, and historically that sort of information has been very fragmented across a variety of system," said Davenport. "They're also bringing that information to the front lines -- even to the cabin crews using iPads -- so it adds up to an impressive effort."
Sojern is collecting and aggregating information across airlines, hotels, rental car agencies and credit card companies and is using machine learning and advanced analytics to develop rich profiles of segments of travelers to determine "when people go, where they go, how many people are travelling, and preferred brands, travel times and class of service," said Rick Farnell, president and co-founder of Think Big Analytics, the Mountain View, Calif.-based consulting firm that helped Sojern with its analytics.
[ Want more on big data analytics? Read Big Data Reboots Real-Time Analysis. ]
Sojern's insights are used by the very same airlines, hotel chains and car rental agencies to hone their pricing and selection of services. "Customers like Delta or Starwood can find all the business travelers that flew between New York and San Francisco over the last month, and it helps them make the right cross-sell and up-sell offers and shape their inventories," Farnell said.
Travelocity applies analytics to pricing, inventory and advertising, and all three dimensions shift on a daily basis depending on supply and demand. It's using techniques like look-alike modeling, next-best-offer analysis and recommendation engines to push the right offers to customers that fit certain profiles.
"They're pre-thinking a lot of patterns that they want to see, but they're constantly changing the experience on a daily basis as demand changes," Farnell said. Hadoop is the environment that handles the big data ingest, he said, and analytic models are then pushed out to the edge applications that trigger decisions in real time.
Multiple airlines are pushing revenue management to the next level by calculating, for example, the value of a group of customers who will miss a connection due to a flight delay and then determining whether to delay their connecting flight or book them on the next plane.
"You have to have a deep understanding of the value of the group, gate availability, the possibility of getting another takeoff slot and so on," said Couturier.
Multiple hotel chains are doing test-and-learn analyses in which they study just how much to spend and where to spend on renovations. High-end hotels, for example, tend to spring for interior renovations whereas low-end, roadside hotels spend on the exterior to attract drive-by guests. But it goes further than that.
"Hotels have generally found that the mid-level renovations are the ones that pay off," said Davenport. "It's different for a Red Roof Inn than a Ritz Carlton, but the returns level off after a certain point."
Many hotel chains have also learned to do dynamic pricing based on the source of the reservation. Priceline customers, for example, tend to spend less on food and beverage services than do Orbits customers, says Davenport, so that can be figured into pricing.
Most uses of big data and advanced analytics fall into three categories: improving internal operations, optimizing pricing and inventory and better serving customers with context. In all three cases the challenge adds up to big data due to the sheer number of destinations, flights, sailings, trains, rooms, prices and days in the year.
"If you multiply all the choices you get trillions of possibilities, and when you search, you want to know exactly and quickly the best fit," said Couturier. "That requires a lot of computing power and change in technology."
That change has included an embrace of grid-based systems for high-speed analytics and open-source platforms such as Hadoop for enriching data with context that was previously unavailable.
"It's no longer just about finding the cheapest or the shortest flight, it's about finding the most-favored flight that has the positive sentiment, the destination with better weather or the better family hotel," Couturier explained.
In a cautionary note for the innovators of today, Davenport pointed out that the airline industry led a wave of innovation back in the late 1970s and early 1980s in areas including revenue management, loyalty management and operational analytics, but there wasn't much of a next act.
"The key takeaway is that you can't be innovative and then sit on those achievements for 20 or 30 years," he said. "Everybody uses those techniques today so they're just a cost of doing business and there's no competitive advantage anymore."
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