Skyline queries have gained attention as an effective way to identify desirable objects that are "not dominated" by another object in the dataset. From market perspective, such objects can be viewed as marketable, as each of such objects has at least one competitive edge against all the other objects, or not dominated. In other words, non-skyline objects are not marketable, as there always exists another product excelling in all the attributes. The goal of this paper is, for such non-skyline objects, to identify the cost-minimal enhancement to become a skyline point to gain marketability. More specifically, we abstract this problem as a mixed integer programming problem and develop a novel algorithm for efficiently identifying the optimal solution. Through extensive experiments using synthetic datasets, we show that our proposed framework is both efficient and scalable over extensive experiment settings.