Share this Post
Most industries have started investing in data analytics and machine learning; the Airline industry is no exception. There has been tremendous increase in air travel over the last decade with around 800 commercial airlines operating flights globally. What was once a luxury and common amongst the high class, is now easily accessible to everyone today. The fierce competition and pricing among airlines started attracting more number of passengers. This was a strategy used to increase the number of travelers. There has been one other strategy used by airlines and other industries alike for many years to retain their customers; frequent flyer or loyalty programs. A program that uses mileage tracking to reward its customer, a marketing technique that was created for customers to stay loyal to a brand.
Frequent flyer programs were one of the first methods airlines used to gather customer data. To stand out from the crowd and gain a loyal customer base, they offered special services like lounge access, duty free shopping etc. to their frequent flyers. When customers signed up for a frequent flyer program, airlines got access to their data. As technology advanced, airlines were able to gather large amounts of data such as the location of the purchase, frequency of travel etc. Once considered key elements of a retention strategy, is now recognized as an efficient way of gathering user data.
While there are certain laws where airlines belonging to a certain geographical boundary (European region) are not permitted to use personal data from historical bookings available within the airline reservation system, other regions who don’t have such strict rules, started building profiles for their customers to market tailored messages to them. Over time airlines started integrating data analytics with loyalty program information to segregate customers based on the number of times they flew and money spent on auxiliary services. Data analytics helped airlines gain insights on customer behavior and preferences by looking at their travel history and spending patterns. By applying predictive analytics to this information, airlines were able to predict customer needs for the future by analyzing when they are likely to plan their next trip. With the help of predictive analysis, airlines can now shoot out customized offers on flight tickets, in-flight services, non-stop flights etc. The more the data, the more the analysis and more the opportunity to target their audience at the right moment.
As time progressed, analytics got integrated with customer data to cater to the ever evolving traveler. To diversify themselves, airlines partnered with other airlines, hotels, car rentals and credit card companies to not just gain further insight on their spending habits, but to offer customized catalogues and services based on the behaviour and preference of the user in terms of food, shopping and other interests. Many airlines offer unique and useful purchasing options; Qantas frequent flyer members for example can use their miles to pay for their healthcare insurance or even buy a Jaguar!
Airlines don’t just want to be known for their air rewards but a brand that offers an assortment of rewards. It’s not just the airlines that have to gain, customers equally benefit from the information they submit; when their data is integrated with analytics, they are kept up-to-date on new offers, they receive alerts about flight delays or gate changes and their custom needs can also be addressed.
Frequent flyer analytics enable win-win partnerships for coalition partners and customers. In a highly competitive market, service providers in many industries have discovered the importance of customer retention. Airlines are investing and focusing more on frequent flyer programs to increase revenue while adding value to the customer’s overall experience by using the data received from different sources and by gaining insights on the customer’s future needs.
IGT’s analytics services offers significant business insights by integrating multiple data sources using a proven methodology, established frameworks, automated tools, and pre-configured data models for the travel industry