Application of Data Mining Techniques to Support Customer Relationship Management At Ethiopian Airlines
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Date
2002-07
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Addis Ababa University
Abstract
The airline industry is highly competitive, dynamic and subject to rap id change. As a result,
airlines are being pushed to understand and quickly respond to the individual needs and wants of
their customers. Most airlines use frequent flyer incentive programs to win the loyalty of their
customers, by awarding points that entitle customers to various travel benefits. Furthermore,
these airlines maintain a database o f their frequent flyer customers.
Customer relationship management (CRM) is the overall process of exploiting customer- related
information and using it to enhance the revenue flow from an existing customer. As part of
implementing CRM, airlines use their frequent flyer data to get a better understanding of their
customer types and behavior. Data mining techniques are used to extract important customer
information from available databases.
This study is aimed at testing the application of data mining techniques to support CRM activities
at Ethiopian Airlines. The subject of this case study is Ethiopian Airlines' frequent flyer
program 's database, which contains individual !"light activity and demographic information of
more than 22,000 program members
The data mining process was divided in to three major phases. During the first phase, data was
collected from different sources since the frequent flyer database lacked revenue data, which was
essential for the study's goal of identifying profitable customer segments. The data preparation on
phase was next, where a procedure was developed to compute and fill-in for missing revenue
values. Moreover, data integration and transformation activities were performed
In the third phase, which is model building and evaluation, K-means clustering algorithm was
used to segment individual customer records into clusters with similar behaviors. Different
parameters were used to run the clustering algorithm before arriving customer segments that
made business sense to domain experts. Next, decision tree classification techniques were
employed to generate rules that could be used to assign new customer records to the segments.
The results from this study were encouraging, which strengthened the belief that applying data
mining techniques could indeed support CRM activities at Ethiopian Airlines. In the future, more
segmentation stud issuing demographic in formation and employing other clustering algorithms
could yield better results.
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Information Science