Reduce travel stress with big data

Recent developments in big data applications use information about consumer habits and demographic trends to develop stress-reducing solutions for travelers. Travel management company CWT recently developed the Travel Stress Index, an algorithm-based tool that they hope will translate information into actionable recommendations, reported InformationWeek’s Ellis Booker.

“We had the transactional data and we had some traveler profile data, but it was scattered and we had to bring them together,” Catalin Ciobanu, CWT big data analyst, told InformationWeek.

Harnessing big data for pointed results involved a multi-faceted approach. They gave surveys to over 7,000 travelers from a variety of backgrounds, who were asked to rank 33 activities on a 1-10 scale, according to the stress they generated. Three categories of stress-inducers came to the forefront: lost time (like not being able to work in locations without wireless or planes without access), surprise (like lost luggage) and interruptions of daily routines (like altered sleeping schedules or a lack of healthy foods). This data was also broken down by demographics, from gender and age to industry and even job title.

“The goal was to calculate the productivity hit caused by stressful travel for different types of travelers,” Booker reported.

Using big data to provide travel solutions
The data CWT analyzed prompted several conclusions. While they observed what Ciobanu called “an irreducible component of stress” that caused nearly 70 percent of surveyed anxieties, they found that 32 percent could be reduced with their help. Using the data collected, they could create sample profiles of what a consumer might be stressed about and respond more directly and personally to a customer’s needs. These possible resources, Booker reported, include offering advice about connecting flights to those made particularly anxious by time constraints and recommending carriers based on lost luggage data for the especially surprise-averse.

According to a CWT press release, advice that confronts the intangibles of business travel can have appreciable financial benefits. The lost time for a firm with an average of 5,000 business trips a year could be as much as $3.3 million, a third of which could be saved through increased traveler productivity.

Other tools, like American Airlines’ ‘abandoned cart’ program, use big data to keep users informed about trips they’ve looked at but haven’t purchased. In addition, Farecast uses databases to predict the optimal time to book a flight and respond to consumer concerns about the cost of air travel. Like CWT’s Ciobanu, business leaders in just about every industry are seeing the value of using big data to develop tools like the Travel Stress Index that utilize clients’ reactions to best meet their needs.

Categorized by :
Big Data Business Analytics

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