The Application of Data Mining to Support Customer Relationship Management at Ethiopian Airlines
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Date
2003-06
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Addis Ababa University
Abstract
Airlines are being pushed to understand and quickly respond to the individual needs and wants of their
customers due to the dynamic and highly competitive nature of the industry. Most airlines use frequent
flyer incentive programs and maintain a database of their frequent flyer customers to win the loyalty of
their customers, by awarding points that entitle customers to various travel benefits.
Customer relationship management (CRM) is the overall process of exploiting customer- related data
and information, and using it to enhance the revenue flow from an existing customer. As part of
implementing CRM, airlines use their frequent flyer databases to get a better understanding of their
customer types and behavior. Data mining techniques play a role here by allowing to extract important
customer information from available databases.
This study is aimed at assessing the application of data mining techniques to support CRM activities at
Ethiopian Airlines. The subject of this case study, the Ethiopian Airlines’ frequent flyer program, has a
database that contained individual flight activity and demographic information of over 35,000 program
members.
Having the objective of filling the gap left by a related research, which was carried out by Henok (2002),
this study has used the data mining database prepared by Henok (2002). In the course of using the
database to attain the objective of this research, a data preparation tasks such as driving new attributes
from the existing original attributes, defining new attributes and then preparing new data tables were
done.
The data mining process in this research is divided into two major phases. During the first phase, since
there has been an attempt to use three different data mining software, data was prepared and
formatted into the appropriate format for the respective data mining software to be used.
The second phase, which is model building phase, was addressed in two sub-phases, the clustering
sub-phase and the classification sub-phase, the major contribution of this study. In the clustering subphase
the K-means clustering algorithm was used to segment individual customer records into clusters
with similar behaviors. In the classification sub-phase, J4.8 and J4.8 PART algorithms were employed
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to generate rules that were used to develop the predestined model that assigns new customer records
into the corresponding segments.
As a final output of this research, a prototype of Customer Classification System is developed. The
prototype enables to classify a new customer into one of the customer clusters, generate cluster
results, search for a customer and find the cluster where the customer belongs, and also provides with
the description of each customer clusters.
The results from this study were encouraging and confirmed the belief that applying data mining
techniques could indeed support CRM activities at Ethiopian Airlines. In the future, more segmentation
and classification studies by using a possible large amount of customer records with demographic
information and employing other clustering and classification algorithms could yield better results
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Keywords
Data Mining