Mining airline data for CRM strategies. (c2006)

Abstract

In today's competitive climate, Customer Relationship Management (CRM) has become an essential component in the airline business strategies. Building CRM in the airline industry requires a comprehensive view of customer behavior. This view has to be based on analyzing customer data in order to understand customer preferences and learn from his/her behavior. In this thesis, we apply data mining techniques to real airline frequent flyer data in order to derive CRM recommendations, and strategies. Clustering techniques group customers by services, mileage, and membership. Association rules techniques locate associations between the services that were purchased. Our results show the different categories of customer members in the frequent flyer program. For each group of these customers, we can analyze customer behavior and detennine relevant business strategies. Knowing the preferences and buying behaviors of our customers allow our marketing specialist to improve campaign strategy, increase response and manage campaign costs by using targeting procedures, and facilitate cross-selling, and up-selling. Furthermore, we explore the characteristics of data mining algorithms for this application and uncover relative merits of the algorithm employed.