With the growing importance of retailing and merchandising to the travel industry, airlines are beginning to resemble retailers like Amazon.com more than their predecessors. Unlike the airlines of the 1980s, today’s carriers offer more than just travel. In fact, many offer loyalty programs, credit cards, insurance, hotel deals and other branded products and services to make your entire travel experience more enjoyable. But this is only the beginning.
To truly capitalize on the value of this retail and merchandising trend, airlines must learn to understand the buying habits and preferences of its customers in the same way retailers like Amazon.com have successfully pioneered. By collecting all available data on a customer’s preferences, needs, buying habits, and other related factors, airlines can offer targeted ancillary products and services that are more likely to result in purchases.
This is accomplished by using both standard analysis and unstructured “big data” analysis to model and predict individual customer behavior. Airlines can then use these results to identify the relevant purchasing DNA in each customer and deliver personalized merchandising offers that extend profitability.
Until recently airlines have used historic customer data to personalize offers. To further extend personalization’s profitability today, airlines can tailor the customer experience through self-learning personalization engines. For example, when a customer arrives at a travel website, they may be shown three types of offers, x, y, and z. If the customer selects offer y, the personalization engine applies machine-learning algorithms to use these types of offers for the customer in the future.
A large amount of today’s personalization online takes place at the transaction level. This is pre-login personalization, where a consumer is shown several complementary products when engaged in a buying decision. This can be effective, but it’s not a very deep level of personalization. Once a user has logged in, we can now deliver a greater degree of personalization. Airlines can combine external data sources, such as purchasing habits derived from credit card companies, to create an even more personalized user experience through an evolving social profile of the customer.
There is also a data collecting process for gaining passenger insights through segmentation. From the time a ticket is purchased, airlines have several opportunities to integrate information from multiple channels, segment profitable customers and profile their behaviors. Once passenger behaviors and preferences are understood, airlines can choose product offers that best fit the passenger’s profile and develop ancillary products based on recommendations.
The diagram below describes the steps from the sale of an airline ticket to the development of preferred ancillary products.
Developing successful ancillary products and services is a process that any airline can perfect based on having the right data and through self-learning technologies. By collecting and analyzing customer data, airlines can offer revenue-generating products that help customers enjoy a more productive travel experience. Let me know your thoughts on this topic.