What’s your customer’s perfect travel experience? Use advanced analytics to find out

airline-customer-experience

Years ago, airline travel was considered glamorous.  People wore suits and dresses. They smoked cigarettes, drank martinis and cut their steaks with metal knives. There was only one version of the perfect travel experience and it was wrapped up in the glamour and prestige of the journey itself — and the personal service supporting it.

Today, things are different.  The perfect travel experience varies drastically depending on who you are and what you are doing.  A couple who buys a business class ticket to Maui for their 20th anniversary is probably looking for a completely different set of services than a college student heading back to Omaha from spring break in a basic economy seat.  Travel is now about the individual experience.

Interestingly, if airlines identified more of their passengers’ situations, contexts and preferences, they could also identify ancillaries, bundles and services the travelers might truly want. Passengers would not only buy these offers more often, they would actually welcome them. This synergy can only happen when offers are tailored to the individual and the specific context.

Targeted offers at the right point of customer contact have been a goal in the travel industry for a number of years.  Airlines know they need intelligent analytics with the ability to “learn” from input provided by knowledgeable staff.  Employing machine learning and advanced analytics across the entire customer context addresses many practical issues:

  • The entire travel itinerary or reason is often not clear to the airline. Think of the knowledge – and the revenue opportunities – gained from understanding the traveler’s journey and ultimate destination, which may include connections to trains, cruises or road trips.  I find it interesting that before I sit down for a nice meal in a restaurant I get asked if it is a special occasion, but not when I buy a $3,000 plane ticket. Knowing the full context of the trip enables the airlines to offer additional travel services, or even upgrades, to enhance the traveler’s experience.
  • Customer sentiment is rarely gathered in its entirety within the airline. Fragmented data spread over many airline departments and third parties makes it almost impossible for carriers even to try to make amends to customers for bad experiences, such as a young mother’s six-hour rolling delay in the Charleston airport with a colicky baby. Greater visibility into all aspects of a traveler’s experience makes it possible to improve the customer relationship.
  • Social sentiment is rarely connected back to the individual traveler’s experience. Admittedly, some people may find it “creepy” or intrusive to have their social media presence tied to their traveler profile.  But would it really be so bad to have the flight crew apologize for the poor service you tweeted about after your last trip with them?
  • Spending behavior is not understood. Sometimes we want the best fare; other times we want comfort.  On some trips we’re in a hurry; on other trips we’re not.  Would airlines really get this looking only at our loyalty status, destination or the time of year?  I think not.
  • Getting timely feedback from the customer at different stages is difficult. Face it, the airline only knows what travelers buy and what they reveal in the occasional limited survey.  Further, customers may be more inclined to complete a survey when they are dissatisfied, rather than when they are happy. Clearly, more feedback mechanisms are needed.  These could include real-time reactions on airline apps, comments from in-flight and ground staff, and various random customer ratings at non-time-critical points of travel like booking and in flight. This immediate feedback could be used to further improve processes and make real-time adjustments where needed.

If travel is about the individual experience, airlines need to get to know their customers on an individual basis so that they can make them feel more like guests with specific travel habits and preferences. Gathering data is only half the issue. The key is to start knitting the data together – profiles, contacts, services, complaints, praise, loyalty, social comments, feedback, app ratings, staff comments – and then apply machine learning to gain insights about the customer.  Understanding the traveler more comprehensively and precisely will help airlines generate new revenue opportunities and increase customer satisfaction.


John Tsucalas is a Distinguished Technologist at DXC and is DXC’s Travel, Transportation and Hospitality Chief Technologist.  He has worked with at least 10 major airlines over his 33-year career as a trusted advisor.  He also has worked in both Manufacturing and Energy industries.  His technology focus for the last several years centers on Automation, IOT, Analytics, and Enterprise Cloud Transformation.

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