For two reasons, marketers face significant challenges in measuring return on marketing investment in business-to-business (B2B) markets. First, buyers often have irregular purchase patterns, as the authors observe in the high-tech industry. Second, marketing efforts take considerable time to build a relationship with a customer. The authors attempt to precisely recover hidden buyer–seller relationship states to capture the effect of marketing contacts in B2B markets. The authors build a comprehensive hierarchical Bayesian bivariate Tobit hidden Markov model to assess the return on marketing in B2B markets. They use a recursive computing method—a forward–backward Gibbs sampler method—to retrieve the relationship states. The results suggest that marketing contacts have a heterogeneous long- and short-term impact on customers' purchasing behavior through changes in the buyer–seller relationship states. This study provides practical value to business marketers to measure the return on marketing investment in buyer–seller relationships.
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