To maximize competitiveness in a low-margin industry, airlines offer an array of products to their customers that extend far beyond basic air travel. Products offered can include flight-related services (checked bags, preferred seating, etc.), itinerary-linked products (hotel, rental car, dining, etc.), and general retail products with which the airline has a cross-sell agreement. The ancillary revenue derived from the sale of these products can be significant, and, for some airlines, accounts for a majority of company profit. Although airlines still provide passenger and cargo transportation as they have for nearly a century, they are rapidly evolving into more generalized retailers of diverse products and services.
Effectively selling the broad range of ancillary products requires up-to-the-minute insight and intelligence into inventory availability and customer preferences; however, both information requirements stand well outside the capabilities of traditional airline IT and analytics. Historically, airlines sold seats on flights (we are ignoring cargo for this discussion), and operated in a “demand pull” environment in which precise knowledge of individual customer preferences was not a critical factor. Further, all of the airline’s inventory (i.e. seats on flights) was available for sale in the carrier’s PSS or GDS. For these ancillary products, the situation is quite different.
In positioning a “supply push” ancillary offering, the information requirements differ dramatically. At the simpler end of the scale, the option to check a bag or purchase a meal onboard can be offered to anyone with a ticket for the flight, and the airline would know exactly how much of the product is available for sale. At the more complex end, however, the situation is much different. An offer for a hotel, a restaurant, or a sporting event must be specifically targeted to a unique segment of the customer universe, potentially to a “segment of one”, as the acceptance rates for each of these offers would vary greatly based upon individual customer preferences. Airlines have not focused on such micro-targeting in the past.
To increase revenue earned from higher-value individualized ancillary offerings, airlines must be selective in the products that they offer to the customer, as well as in all other related decisions regarding the offer’s timing, price, etc. Such decisions can be based upon the analysis of data specific to the customer, including flight and destination details, past travel history, preferences, purchasing habits, etc. However, airlines maintain only a small amount of the required data in their internal systems. To fully characterize the individual customer, additional data must be identified and analyzed.
In short, the airline’s task is to collect all available data on a customer’s preferences, needs, buying habits, and other related factors, and position a product offering to that customer so as to maximize the likelihood that the offer will be accepted and the product purchased. Retailers have pioneered the methods of understanding customer behavior at the finest level of detail, and airlines can utilize many of the same practices.
In addition to the airline’s own historical data on passenger behavior, other data sources can be accessed and evaluated. These sources include credit card data, GDS, social media, etc. A vast amount of data is potentially available for many of the airline’s customers through publicly available as well as purchased data sources. The various data sources have diverse characteristics in terms of size, information, structure, completeness, value, etc. Analytical tools have been refined to the point where “big data” techniques can overcome many of the obstacles posed by the diversity in the data characteristics and yield valuable analytical results.
The information from these sources can then be used to build a purchasing profile for an individual customer, and then to evaluate that profile for signals and indications as to which product offerings are most likely to be accepted.