Meshesha, Million (PhD)Girma, Melaku2020-06-102023-11-182020-06-102023-11-182009-01http://etd.aau.edu.et/handle/12345678/21513in 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.enInformation ScienceThesis