Abstract—Grouping items facilitates ideation. Although Cluster Analysis has become a classical technique for grouping in science and engineering, to the best of our knowledge it's use remains limited in business. In this application paper we use cluster ensembles to address three barriers to wide scale adoption in the banking industry. The aforementioned challenges are: consistency of results, knowledge beyond the data and grouping with multiple objectives. Contributions of this study include guidance on dealing with the lack of meaningful cluster labels (in the case of ensembles), bimodal cluster distributions and incorporating expert intuition into the clustering process. This application has delivered unobvious insight into a high-dimensional dataset to audiences with diverse backgrounds.