Modeling Behavioral Regularities of Consumer Learning in Conjoint Analysis

Abstract

In this note, we propose several extensions of the model of consumer learning in conjoint analysis developed by Bradlow, Hu, and Ho (2004). We present clarification of the original model; propose an integration of several new imputation rules; add new measurement metrics for pattern matching; and draw a roadmap for further real-world tests. We discuss general modeling challenges when one wants to mathematically define and integrate behavioral regularities into traditional quantitative domains. We conclude by suggesting several critical success factors for modeling behavioral regularities in marketing. We welcome the constructive comments on our paper (Bradlow, Hu and Ho 2004; BHH hereafter) by Alba and Cooke (2004), Rao (2004), and Rubin (2004). Since a major goal of our paper is to enrich conjoint analysis with a stronger behavioral foundation, we are pleased to hear from our colleagues in Marketing, all of whom have both behavioral modeling and quantitative interests, and from Rubin, who first introduced the formal nomenclature of missing data methods into the statistics literature (Rubin 1976). We believe such dialogue will allow us to harness the strengths of varied research paradigms and make marketing theories more precise and predictive of actual consumer behavior. We would like to organize our responses to the three comments along four subsections. The first section includes general responses that touch on the issues of research language and mathematical formalism and the last three are specific responses to the comments in terms of clarification, additional data analyses, and model extensions.