The author discusses practical concerns in analyzing MBC studies. He highlights the value of using randomized designs for MBC questionnaires, as they allow for straightforward counting analysis. Counting analysis helps the analyst decide which effects (own and cross-effects) are important to include in subsequent modeling. Three different approaches for building logit-based simulators are discussed: 1) Volumetric CBC Model, 2) Exhaustive Alternatives Model, and 3) Serial Cross-Effects Model. Both aggregate logit and HB models are estimated. All approaches worked quite well for the data set described here. The Volumetric CBC Model is the easiest to perform, but has weaknesses and is not a theoretically sound approach. The Exhaustive Alternatives model is more theoretically pleasing, but practically is limited to use with smaller menu problems that don't include very many options on the menu. The Serial Cross-Effects model strikes a good balance between flexibility and soundness.