  The application of data mining technique has been widely applied in different
business areas such as health, education and finance for the purpose of data
analysis and then to support and maximizes the organizations customer
satisfaction in an effort to increase loyalty and retain customers business
over their lifetimes . The researchers primary objective, in this paper is to
classify customers based on their common attributes since customer grouping is
the main part of customer relationship management. In this study, different
characteristics of the ERCA customers data were collected from the customs
database called ASYCUDA. Once the customers data were collected, the necessary
data preparation steps were conducted on it and finally a data set consisting
of 46748 records was attained. The classification modeling was built by using
J48 decision tree and multi layer perceptron ANN algorithms with 10-fold
cross-validation and splitting (70% training and 30% testing) techniques. Among
these models, a model which was built using J48 decision tree algorithm with
default 10-fold cross-validation outperforms 99.95% of overall accuracy rate;
while the classification accuracy of ANN is 99.71%. So decision tree has better
accuracy than ANN for classifying ERCA customers data.
