Dynamic Targeted Pricing in B2B Settings
- Dynamic Targeted Pricing in B2B Settings
- Zhang, Zaozao
- Thesis Advisor(s):
- Ansari, Asim M.
- Persistent URL:
- Ph.D., Columbia University.
- This research models the impact of firm pricing decisions on different facets of the customer purchasing process in business-to-business (B2B) contexts and develops an integrated framework for inter-temporal targeted pricing to optimize long-term profitability for the firm. Pricing decisions in B2B settings are inherently different from those within the business-to-consumer (B2C) environment, commonly studied in marketing. First, B2B pricing often offers considerable flexibility in implementing first degree and inter-temporal price discrimination, i.e., sellers in B2B contexts can easily vary prices across customers and can even change prices between subsequent purchases by the same customer. While this flexibility affords significant opportunities for the firm, it also requires great care in ensuring long-term profitability. Second, transactions in the B2B environment are often more complex than those in B2C settings. Specifically, the business customer typically makes several interrelated decisions (e.g., when and how much to buy, whether to ask for a quote and whether to accept the seller's bid), which need to be modeled jointly. The proposed model considers these inter-related decisions in an integrated fashion. In addition, the model accounts for heterogeneity in customers' preferences and behaviors, asymmetric reference price effects, and purchase dynamics, while taking into account the short- and long-term implications of the pricing policy. To model the complexity of inter-related joint customer decisions, we use hierarchical Bayesian copulas, which weave together different marginal distributions to form joint distributions. To account for dynamics in purchase behavior and to model the possible long-term impact of experienced prices on the different components of the customer's decision, we use a non-homogenous hidden Markov model with multivariate interrelated state-dependent behaviors. In addition, we rely on the behavioral pricing literature in modeling the effect of price, using asymmetric reference price effects. We calibrated the model using longitudinal transaction data from a metals retailer. The results reveal several substantive insights about the short- and long-term impact of the firm's pricing decisions on each of the inter-related components of the customer's purchasing behavior. Specifically, we find positive interdependence between the quantity and purchase timing decisions and strong negative interdependence between the decision to request a quote and the decision to accept it. Capturing the long-term and asymmetric impact of reference prices, we find that losses not only have larger negative effects relative to gains on customers' buying behavior, but customers also take longer to adapt to losses than they do to gains. Furthermore, the firm's pricing decisions could have a long-term impact on its customers by shifting their preferences between a "vigilant" state - characterized by a cautious approach towards ordering and heightened price sensitivity, and a more "relaxed" state. These dynamics imply that the B2B seller needs to carefully consider both the short- and the long-term consequences of its pricing policy when setting prices for each order. Additionally, the proposed model exhibits superior predictive performance relative to several benchmark models, and in a price policy simulation results in a 52% improvement in profitability compared to the company's current practice. Through pricing simulations performed are made when pricing in volatile economic environments. Other policy simulations are conducted to examine how the B2B firm should price in the recent economic environment with volatile commodity prices. We find when commodity prices increase, the firm should pass the costs to the customers, when the prices decrease, the firm should "hoard" the profit.
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- Suggested Citation:
- Zaozao Zhang, 2011, Dynamic Targeted Pricing in B2B Settings, Columbia University Academic Commons, http://hdl.handle.net/10022/AC:P:10178.