Marketing practitioners choose to run individual-level models (HB) to capture heterogeneity in preferences when it comes to trade-offs. But the majority of designs built for choice research are focused on improving the insights at the aggregate level. Our hypothesis is that there are better design strategies to capture individual heterogeneity and ultimately improve model predictions, particularly when it comes to looking at subgroups within the data. This paper will explore several methods of optimizing designs including relabeling-swapping routines and utility balanced designs and give the audience access to an open-source package, built in Julia by the Numerious team, to build these types of designs.
