Applicability of Data Mining Techniques to Customer Relationship Management (Crm): The case of Ethiopian Telecommunications Corporation's (ETC) Code Division Multiple Access (CDMA) Telephone Service
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Date
2009-01
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Addis Ababa University
Abstract
in this research the applicability of clustering and classification techniques of data mining on CRM
the case of COMA telephone service of ETC have been explored within the framework of CR[SPOM
model. The COMA COR data along with billing information and the customers' profiles are
collected, cleansed, transform ed and integrated for experiment renting with the clustering models. The
final datasets consists of [0,090 records on which different clustering models at K values o f 6, S,
and 4 with different seed values have been experimented and evaluated against their performances.
Hence, the cluster model at K value of 6 has shown a better performance. Consequently, its output
is used as an input for the decision tree and ANN c lass ifi cation models.
First the different classification models with J48 decision tree algorithm are experiment en ted with the
IO-fold cross validation, and splitting the datasets o 80 % training an d 20 % testing, techniques
by setting the cluster ind ex formed by the cluster model as dependent variable and the rest as
independent variables. Among these models model that showplace ossification accuracy of
98.97% is selected . Similarly, different classification models of multilayered ptron ANN
algorithm are carried out by Chang in g its hidden layer number of nodes a learning's rate
parameters' value. A model with a classification accuracy of 98.62 % is chosen. Finally a
comparison o f decision tree and ANN mo de ls in terms of the overall class unification
accuracy,
accuracy In classifying hi g h value customers, and accuracy in c lass glowing value customers
ha ve been undertaken. Hence, the decision tree model has excelled in th ese evalu ation parameters
and therefore selected as the best classifier for CRM applications.
The result of this research is really encouraging as very high class if ication accuracy has been
obtained. Besides, hi precede vision and recall in c lass unifying high and low value customers correctly
have been achieved.
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Information Science