in this research the applicability of clustering and classification techniques of data mining on CRMrnthe case of COMA telephone service of ETC have been explored within the framework of CR[SPOMrnmodel. The COMA COR data along with billing information and the customers' profiles arerncollected, cleansed, transform ed and integrated for experiment renting with the clustering models. Thernfinal datasets consists of [0,090 records on which different clustering models at K values o f 6, S,rnand 4 with different seed values have been experimented and evaluated against their performances.rnHence, the cluster model at K value of 6 has shown a better performance. Consequently, its outputrnis used as an input for the decision tree and ANN c lass ifi cation models.rnFirst the different classification models with J48 decision tree algorithm are experiment en ted with thernIO-fold cross validation, and splitting the datasets o 80 % training an d 20 % testing, techniquesrnby setting the cluster ind ex formed by the cluster model as dependent variable and the rest asrnindependent variables. Among these models model that showplace ossification accuracy ofrn98.97% is selected . Similarly, different classification models of multilayered ptron ANNrnalgorithm are carried out by Chang in g its hidden layer number of nodes a learning's raternparameters' value. A model with a classification accuracy of 98.62 % is chosen. Finally arncomparison o f decision tree and ANN mo de ls in terms of the overall class unification rnaccuracy,rnaccuracy In classifying hi g h value customers, and accuracy in c lass glowing value customersrnha ve been undertaken. Hence, the decision tree model has excelled in th ese evalu ation parametersrnand therefore selected as the best classifier for CRM applications.rnThe result of this research is really encouraging as very high class if ication accuracy has beenrnobtained. Besides, hi precede vision and recall in c lass unifying high and low value customers correctlyrnhave been achieved.