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.
