In direct marketing, in order to increase the return rate of a marketing campaign, the massive customer dataset is needed to be analyzed, to make best product offers to the customers through the most proper channels. However, this problem is very challenging, since, usually only for very small portions of the whole dataset, some positive returns are received. This paper studies the similar problem for bank product marketing. The proposed approach is a two layer system, which first clusters the customers and then, constructs a classification model for product and communication channel offers. Experimental analysis on real life banking campaign dataset shows promising results.