Identifying customers who are more likely to res pond to a product offering is an important issuernin direct marketing. In direct marketing, data mining has been used extensively to identifyrnpotential customers for a new product (target selection) .rnThe final goal of this thesis is to build a model that helps to classify the book customers o frnDASH EN BAN K SC, according to their expected response to the direct marketing campaign.rnSince there are no predefined classes, that describe the customers of the bank according to theirrnexpected response to visa card offer, the researcher uses a clustering techniques that resulted inrnthe appropriate number of clusters. Then, a predictive response model was developed to predictrnthe degree of likelihood (as high, medium and low) that a customer is go in g to respond to a visarncard offer This predictive mod el achieved accuracy o f 96. 14%.rnFor modeling purpose customers' data was gathered fro m DASHEN BAN K S .c. Since irrelevantrnor redundant features result in bad model performance, data preparation like attribute selectionrnwas performed in order to determine the inputs to the mo del.rnThus various data mining techniques and algorithms were used to implement each step of thernModeling process and alleviate related difficulties . K-M answers used as a clustering algorithm tornsegment customers ' record into clusters with similar characters . Different parameters we re usedrnto run the clustering algorithm be fore reaching at segments that made business sense. J4 8rndecision tree algorithm was used for classification purpose. In addition to those attributes that arernbelieved by the expert s to have high impact on customers probability to be the potential customerrnof the visa card service, this research found the attributes " Occupation" and " Accommodation" tornhave a big influence. Moreover, with respect to the attribute " Age" a new pattern was found rn. Generally the result of the stud y was encouraging, which reinforce the possible application ofrnData mining solution to the banking in dusty , particularly in direct marketing campaign at thernDASH EN BANK S,c.