Dynamic Targeted Pricing in B2B Settings
type: CreativeWork
description: 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 t…
abstract: 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 t…
author: Zhang, Zaozao
language: English
keywords: Marketing, Pricing--Decision making, Pricing--Econometric models, Statistics, Economics, Markov processes, Bayesian statistical decision theory
date published: 2011
date modified: 2026-07-24T01:44:11Z
provider: Columbia University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Columbia University

PropertyValue
property ID: DOI
value: 10.7916/D8S188D7