We argue in favor of bespoke, scalable MCMC algorithms for posterior inference in some canonical hierarchical model classes, and describe such an algorithm for crossed effects models. We numerically demonstrate the success of the algorithm in competition with off-the-shelf MCMC and VB methods on an election forecasting problem.