Product attributes and models of multiple discreteness

Demand for product characteristics is examined within the context of models that allow for both corner and interior solutions corresponding to zero and non-zero demand. Product attribute information is associated with marginal utility and curvature (satiation) parameters of various utility functions. Empirical applications demonstrate the need for incorporating characteristics in a fairly general way. We also compare our approach to an ideal point and pure Lancasterian versions of our nonlinear utility model. The data support our model over either the ideal point or Lancasterian variants.     

Structural models of complementary choices

This article reviews the rapidly growing literature on structural models of complementary choices. It discusses recent modeling developments and identifies promising areas for future research.     

Reply to Comments on “Bayesian Analysis of Simultaneous Demand and Supply”

Quantitative Marketing and Economics -     

Modeling marketplace behavior

Marketplace behavior refers to aspects of the purchase behavior of individuals and firms that leads to marketplace demand. It is characterized by the presence of many variables, most of which have nothing to do with a specific venture or specific consumer. This paper discusses three challenges of analysis commonly found in quantitative models of marketplace data: heterogeneous consumers, goal-directed behaviors, and the selective attention to some but not all variables in extended models of behavior. Bayesian solutions to these challenges are discussed.     

Adjusting Choice Models to Better Predict Market Behavior

The emergence of Bayesian methodology has facilitated respondent-level conjoint models, and deriving utilities from choice experiments has become very popular among those modeling product line decisions or new product introductions. This review begins with a paradox of why experimental choices should mirror market behavior despite clear differences in content, structure and motivation. It then addresses ways to design the choice tasks so that they are more likely to reflect market choices. Finally, it examines ways to model the results of the choice experiments to better mirror both underlying decision processes and potential market choices. Co-chairs. Author order is alphabetical.     

Benefit Formation and Enhancement

Quantitative Marketing and Economics - The identification of product attributes and features that are essential for a benefit to exist is critical to new product development and formulation. Some...     

A threshold model for respondent heterogeneity

Continuous models of respondent heterogeneity assume the existence of a response function where variables of interest are continuously related to explanatory variables. In many situations this assumption may not be true. In this paper we propose an approach of modeling respondent heterogeneity that identifies abrupt changes in the distribution of response coefficients around a threshold specification. Our model differs from traditional threshold models by introducing the threshold effect to describe across-unit behavior as opposed to within-unit behavior. We illustrate our proposed Bayesian threshold model for survey data from a large national retail bank that examines the effects of service wait times on customer satisfaction. We find evidence of a threshold effect where long in-process wait times are associated with bank branches characterized by weak associations between service quality drivers and overall perceptions of service quality. Branches with wait times below the threshold are found to have much stronger associations.     

A Model for Observation,Structural, and Household Heterogeneity in Panel Data

Standard methods of understanding customer behavior in marketing allow for differences in sensitivity across consumers, but often assume that the sensitivity of a particular individual is fixed through time. In many situations, this assumption may not be valid. Both the importance of variables, and the manner that they are combined to form an overall measure of value for an offer, can change. In this paper we propose an approach of modeling a customers purchase history that allows identification of when these aspects of customer behavior are likely to change. This information is useful, for example, in planning when particular customers will be most likely to respond to an offer. Our approach nests common methods of dealing with individual differences, and allows for the introduction of covariates associated with changes in customer behavior. We illustrate our model with data from a national sample of credit card usage and adoption.