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Ask yourself this: Is it worth it to tack on two hours to your flight from JFK to SFO if it means saving $2?
If you look at any number of OTA flight-search systems, the answer would be yes. That’s because “historically, everything has been driven to try to show people the lowest cost and most variety,” says Eric Bailey, senior travel manager of strategy and technology at Microsoft. “But there is pushback because that is not productive. Saving someone $2 because that adds two hours to their trip doesn’t do anyone any good.”
Instead, OTAs would be wiser to serve different options to different users by relying on any number of data sets available. Armed with this data, these sites could customize and serve more relevant content to each user based on an understanding of each user’s travel preferences and behaviors.
Data points might include previous destinations, flight times and rates as well as demographical data such as postal code, the type of credit card or other forms of payment typically used. What’s the user’s estimated income? Has the user clicked on certain ads? Does the customer plan trips for himself or others, how long are these trips typically, and are they generally weekend or for longer stays?
“There are all of these different things that they can use,” says Harteveldt, “so that they can start to build a profile of you and then if they overlay that with data from a third party company then they can figure out more of who you are as a consumer.”
Amazon’s analytics algorithm is a good example of this kind of approach. It serves up, upon each visit, recommendations based on past purchases. This is one of the biggest benefits OTAs and those in the travel and hospitality space can glean from Big Data. With less choice, but more relevant choice, shoppers have an improved booking experience.
This is not “about restricting access,” says Kelly McGuire, Executive Director of the Hospitality and Travel Global Practice at SAS. “It’s just showing you something you are likely to be interested in so you don’t see something you are not. The benefit to the consumer is time, and the benefit to the hotel or airline is conversion.”
This thinking carries over to the Ad Targeting space. Most online travel ads contain messaging based on an aborted search, and these ads are served to the user both onrand off-site. So, for example, if a user clicks several steps into the search process for a trip to Boston, but leaves the site before booking, she will see likely generic ads for Boston travel on many sites she subsequently visits.
OTAs would be smarter to cull data based on many more data sets to use online advertising more effectively by further tailoring it to the user, with offers or content based on individual characteristics such as income, time of year most traveled, travel companions typically in tow. That means that if the woman searching for flights to Boston typically travels with her children and spouse in the Spring, and opts for more budget-friendly hotels, she may see ads for family travel to Boston, or even ads for family travel at similar U.S. cities with wallet-friendly deals on offer.
“There is a mechanism for identifying the person when they arrive at the web site whether I ID myself or they match me to a previous anonymous session or assign some sort of profile for people like me,” says McGuire. “Then, based on what you know of my past history added to my current activity, predict what kind of content would be attractive to me, predict an offer that would be attractive to me, guide me to what I am looking for.”
Dave O’Flanagan, CEO of Boxever, supplier of a platform that integrates Big Data into a travel company’s marketing efforts, says customer acquisition costs on paid media can be reduced by 21% by understanding more about the customer and serving them more relevant marketing materials.
An added benefit? A 17% jump in conversion on cross-sells, such as added hotel or car reservations.