Mining airline data for CRM strategies

Abstract

In this paper, we apply data mining techniques to real airline frequent flyer data in order to derive customer relationship management (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 determine relevant business strategies. Knowing the preferences and buying behaviors of customers allow marketing specialists to improve campaign strategy, increase response and manage campaign costs by using targeting procedures, and facilitate cross-selling, and up-selling.