Dynamic Targeted Pricing in B2B Relationships

We model the multifaceted impact of pricing decisions in business-to-business (B2B) relationships that are governed by trust. We show how a seller can develop optimal intertemporal targeted pricing strategies to maximize profits over time while taking into consideration the impact of pricing decisions on short-term profit margin, reference price formation, and long-term relationships. Our modeling framework uses a hierarchical Bayesian approach to weave together a multivariate nonhomogeneous hidden Markov model, buyer heterogeneity, and control functions to facilitate targeting, capture the evolution of trust, and control for price endogeneity. We estimate our model on longitudinal transactions data from a retailer in the industrial consumables domain. We find that buyers in our data set can be best represented by two latent states of trust toward the seller—a “vigilant” state that is characterized by heightened price sensitivity and a cautious approach to ordering and a “relaxed” state with purchase behaviors that are consistent with high relational trust. The seller's pricing decisions can transition buyers between these two states. An optimal dynamic and targeted pricing strategy based on our model suggests a 52% improvement in profitability compared with the status quo. Furthermore, a counterfactual analysis examines the seller's optimal pricing policy under fluctuating commodity prices.