We model the multifaceted impact of pricing decisions in B2B contexts and show how a seller can develop optimal inter-temporal targeted pricing strategies to maximize long-term customer value. We empirically model the B2B customer's purchase decisions in an integrated fashion. In order to facilitate targeting and to capture the short and long-term dynamics of B2B customer purchasing, our modeling framework weaves together in a hierarchical Bayesian manner, multivariate copulas, a non-homogeneous hidden Markov model, and control functions for price endogeneity. We estimate our model on longitudinal transactions data from an aluminum retailer. We find that customers in our dataset can be best represented by two latent states — a "vigilant" state characterized by heightened price sensitivity and a cautious approach to ordering, and a more "relaxed" state. The seller's pricing decisions can transition customers between these two states. An optimal dynamic and targeted pricing strategy based on our model suggests a 52% improvement in profitability compared to the status quo. Furthermore, a counterfactual analysis which examines the optimal policy under fluctuating commodity prices reveals that the seller should pass much of the costs to customers when commodity prices increase, but hoard most of the profit when commodity prices (seller's costs) decrease.