I will disagree with Eric on this book being a must-have for any "applied quantitative" statistics or marketing Ph.D. student, and call it a must-see for people interested in Bayesian discrete-choice modeling. The five case studies are all examples of marketing research, but are relevant to a much broader audience - consider, for example, "scale usage heterogeneity", affecting analysis of rating-scale responses. The case-study chapters are the book's forte, but it also offers a proper and rigorous introduction to Bayesian modeling, including the expected topics such as simulation (MCMC, Gibbs sampler, etc.) and linear regression, but also chapters on HLM, endogeneity, and model selection. The authors discuss doing Bayesian computation with R package bayesm, but regrettably relegate R material to appendices instead of integrating it into the main narrative and making implementation transparent and reproducible.