Classification schemes such as taxonomies are important groundwork for research on many topics in Information Systems (IS) and Management. They make investigating topics manageable by allowing researchers to delimit their work to certain taxa or types and provide a basis for generalization. Opposed to theoretically grounded typologies, taxonomies are empirically derived from entities of a phenomenon under investigation and therefore have several advantages such as more detailed and exhaustive coverage. Nevertheless, research is still missing a clear set of procedures on how to empirically build taxonomies. We tackle this topic by suggesting an inductive approach based on the procedures of content and cluster analysis. Each of the proposed six steps is amended with comprehensive state of the art guidelines, suggestions, alternatives and formative measures of reliability and validity.