This chapter examines the use of choice models in marketing. After briefly describing the genesis of choice modeling, we introduce the two basic workhorses in choice modeling, the logit and probit models. We use these two models as a platform from which to show how additional phenomena can be introduced, including multistage decision processes, dynamic models, and heterogeneity. After a description of some more advanced models, we close by illustrating how these models may be used to provide insight to marketing managers by discussing a number of choice modeling applications.
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John Roberts and Denzil G. Fiebig
In this chapter I present three techniques—Cluster analysis, factor analysis, and multidimensional scaling—popular with marketing researchers and consultants because they help achieve frequently encountered marketing goals. Cluster analysis is useful in finding customer segments, factor analysis is useful for survey research, and multidimensional scaling is useful in creating perceptual maps.
Jorge Silva-Risso, Deirdre Borrego and Irina Ionova
We develop a consumer response model to evaluate and plan pricing and promotions in durable good markets. We discuss its implementation in the US automotive industry, which “spends” about $50 billion each year in price promotions. The approach is based on a random effects multinomial nested logit model of product and transaction-type choice. Consumers differ in their overall price sensitivity as well as in their relative sensitivity to alternative pricing instruments which has to be taken into account to design effective pricing programs. We estimate the model using Hierarchical Bayes methods to capture response heterogeneity at the local market level. We illustrate the model through an empirical application to a sample of data drawn from J.D. Power transaction records.
Zoë Chance, Ravi Dhar, Michelle Hatzis, Michiel Bakker, Kim Huskey and Lydia Ash
In this chapter, we share the 4Ps Framework for Behavior Change, designed to organize research findings to make them more easily applicable in the real world. We offer levers the well-meaning planner can employ to support the healthy intentions of others, and share examples of how the 4Ps Framework is being applied at Google. Although our examples focus on nudging people toward healthy food choices, similar strategies can be used to nudge people’s behavior in any direction that supports their own intentions. We offer advice for influence one-time decisions via (1) the combination of choices offered, (2) the choice environment, and (3) communication about the choices. We also offer advice on supporting individuals in the development of good habits, to make better choices in any time or place.
Edited by Natalie Mizik and Dominique M. Hanssens
Natalie Mizik and Eugene Pavlov
We review panel data models popular in marketing applications and highlight some issues, potential solutions, and trade-offs that arise in their estimation. Panel data studies controlling for unobservables often show dramatically different estimates than cross-sectional studies. We focus on models with unobservable individual-specific effects and address some misconceptions appearing in marketing applications.
Natalie Mizik and Robert Jacobson
We illustrate the application of dynamic panel data methods using the direct-to-physician (DTP) pharmaceutical promotions data described in an article by Mizik and Jacobson (2004). Specifically, we focus on using panel data methods to determine appropriate model specification and to demonstrate how dramatically the estimates of the DTP effectiveness change across various common model (mis)-specifications.
Rahul Guha, Darius Onul and Sally Woodhouse
We outline some basic considerations and implementation strategies regarding the use of consumer surveys and conjoint analysis in the context of complex litigation. We also describe two applications of these techniques in antitrust disputes in the payment card and infant formula supplements industries.
Online advertising has grown rapidly in recent years. The rise of this new form of advertising has generated a number of policy questions around privacy, the ability of local governments to regulate information, and antitrust in online markets. This chapter reviews three studies using a combination of field experiments and quasi-experimental variation to answer policy questions related to online advertising.
Pradeep K. Chintagunta
In this chapter, I provide brief discussions of what we mean by structural models, why we need them, the typical classes of structural models that we see being used by marketers these days, along with some examples of these models. I provide a basic discussion of structural models in the context of the marketing literature and limit myself largely to models of demand rather than models of firm behavior.