We consider in this paper the customer-oriented catalog segmentation problem that consists of designing K catalogs, each of size r products that maximize the number of covered customers. A customer is covered if he/she has interest in at least the specified minimum number of products in one of the catalogs. The problem addresses the crucial issue of the design of the actual contents of the catalogs that serves as a back-end to catalog production for the purpose of more focused design of catalogs as a targeted marketing tool. We developed two algorithms to solve the problem. Results of an extensive computational study using real and synthetic data sets show that one of the proposed algorithms outperforms the state-of-the-art algorithm found in the literature in terms of customer coverage, resulting potentially in significant increase in organization profit. In the spirit of the guidance role that a Decision Support System (DSS) should play by recommending alternative, satisfactory solutions to the decision maker, the prototype of a DSS integrating all three algorithms is presented to provide the decision maker with an easy-to-use, yet powerful tool to examine various catalog design options and their implications on the contents of the catalogs and the clusters of covered customers.
