Accurate and effective discovery of a brand's key assets, namely, Key Opinion Leaders (KOLs) and potential customers, plays an essential role in marketing campaigns. In a massive online social network, brands are challenged with identifying a small portion of key assets over an enormous volume of irrelevant users, making the problem a highly imbalanced one. Moreover, having to deal with social media data that are usually high-dimensional, the task of brand key asset discovery can be immensely expensive yet inaccurate if the information are not processed efficiently to extract representative features from the original space prior to the learning process. To address the above issues, we propose a novel method dubbed Cluster-wise Biased Discriminant Projection (CBDP) to uncover the compact and informative features from users' data for brand key asset discovery. CBDP conducts a two-layer learning procedure. In the first layer, a Discriminant Clustering (DC) scheme is developed to partition the original dataset into clusters with maximum discriminant capacity. In the second layer, a Biased Discriminant Projection (BDP) algorithm is proposed and performed on each cluster to map the high-dimensional data to the low-dimensional subspace, where the discriminant information of classes with high importance/preference is preserved. A unified mapping function of CBDP is finally established by integrating these two layers. Experiments on both synthetic examples and a real-world brand key asset dataset validate the effectiveness of the proposed method.