Marketing

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.

This paper focuses on add-on sales to determine both their value per se and their value as a reflexive measure of brand equity. Specifically, this paper examines the "accessory premium" for automobiles, i.e., accessories installed by dealers at the time of sale. Using J.D. Power Data, the authors find that higher add-on accessory sales accrue to higher equity brands which make accessory sales a potentially useful measure of brand equity (J. Marketing 67: 1?17, 2003). In addition, the authors examine how the revenue premium metric varies by age cohort.

We propose a framework for the joint study of the consumer's decision of where to buy and what to buy. The framework is rooted in utility theory where the utility is for a particular channel/brand combination. The framework contains firm actions, the consumer search process, the choice process, and consumer learning. We develop research questions within each of these areas. We then discuss methodological issues pertaining to the use of experimentation and econometrics.

We study consumers' click behavior on organic and sponsored links after a keyword search at an Internet search engine. Using a dataset of individual-level click activity after keyword searches from a leading search engine in Korea, we find that consumers' click activity after a keyword search is quite low and is heavily concentrated on the organic list. However, searches of less popular keywords (i.e., keywords with lower search volume) are associated with more clicks per search and a larger fraction of sponsored clicks.

We propose an approach for using individual-level data on social interactions (e.g., number of recommendations received by consumers, number of recommendations given by adopters, number of social ties) to improve the aggregate penetration forecasts made by extant diffusion models. We capture social interactions through an individual-level hazard rate in such a way that the resulting aggregate penetration process is available in closed form and nests extant diffusion models.