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Trajectories of attention during a complex brand choice task reflect the accumulation of utility and predict final choice before consumers implement it. Our findings reveal a “double attention lift” of the ultimately chosen brand towards the end of the choice task: it receives more attention than other brands do, and more of that attention is devoted to integrating information about the brand rather than to comparing it with other brands. Attention trajectories predict 85% of brand choices correctly out-of-sample, and 52% one period (29 secs.) before consumers reveal their brand choice. Attention during the choice task reflects sources of utility over and above those from brand ownership and knowledge effects. These results are obtained from a new multivariate attention-and-choice model, using K-fold Cross-Validation, and data from a large scale eye-tracking experiment among 325 regular consumers. The findings support notions from rational inattention theory, reveal the tight, potentially neurological, link between attention and utility accumulation, and have implications for consumer choice theory and managerial practice.

This paper examines the effects of brand credibility, a central concept in information economics–based approaches to brand effects and brand equity, on consumer choice and choice set formation. We investigate the mechanisms through which credibility effects materialize, namely, through perceived quality, perceived risk, and information costs saved. The credibility of a brand as a signal is defined as the believability of the product position information contained in a brand, which depends on consumer perceptions of the willingness and ability of firms to deliver what they have promised. The choice set is defined as the collection of brands that have a nonzero probability of being chosen among those actually available for choice in a given context. Furthermore, we study the impact of brand credibility on the variance of the stochastic component of utility. Not only do choice model parameters capture the impact of systematic utility differences on choice probabilities, but also the magnitude of this systematic impact is moderated by the relative importance of the stochastic utility component in preference. We term this moderation phenomenon preference discrimination, which we conceptualize as the decision makers' capacity to effectively discriminate between products' utilities in choice situations. We estimate a discrete choice model of brand choice set formation and preference discrimination on experimental data in two categories—juice and personal computers—and find strong evidence for brand credibility effects and differential mechanisms through which brand credibility's impact materializes on brand choice conditional on choice set, choice set formation, and preference discrimination.

This article empirically studies consumer choice behavior with respect to store brands in the United States, the United Kingdom, and Spain. Store-brand market shares differ by country and are usually much higher in Europe than in the United States. The authors study the notion that the differential success of store brands in the United States and Europe is the higher brand equity that store brands command in Europe than in the United States. They use a framework based on consumer brand choice under uncertainty and brands as signals of product positions to conduct their analysis. More specifically, they examine whether uncertainty about quality (or the positioning of the brand in the product space); perceived quality of store brands versus national brands; consistency in store-brand offerings over time; and consumer attitudes toward risk, quality, and price underlie the differential success of store brands at least partially in the United States and Europe. The authors’ model is estimated on scanner-panel data on laundry detergent in the U.S., U.K., and Spanish markets and on toilet paper and margarine data in the U.S. and Spanish markets. The authors find that consumer learning and perceived risk (and associated brand equity), as well as consumer attitude toward risk, quality, and price, play an important role in consumers’ store-brand and national-brand choices and contribute to the differences in relative success of store brands across the countries studied.

Brands can affect various stages of consumer choice processes, and hence, various components of consumer utility functions. Previous conceptual and empirical work focused on the effects of brands on consumer perceptions of tangible and intangible product attributes. In this paper, we extend the work on brand effects with information economics underpinnings to analyze whether consumer price sensitivity, that is, the weight attached to price in a consumer valuation of a product's overall attractiveness or utility, is impacted by brand credibility. In other words, we investigate how the impact of product price on consumer utility is moderated by brand credibility. To explore the impact of brand credibility on consumer price sensitivity across categories that may involve different levels of consumer uncertainty, we conduct our analysis for four products: frozen concentrate juice, jeans, shampoo and personal computers. These categories vary in the degree of potential consumer uncertainty about product attributes, as well as in a number of other category-specific features that may affect consumer sensitivity to uncertainty. Our results indicate that brand credibility decreases price sensitivity. Our results also indicate that although the direction of the impact is the same, the magnitude of brand credibility's impact on consumer choices and price sensitivity vary across product categories, as a function of product category characteristics that affect potential consumer uncertainty and consumer sensitivity to such uncertainty.

This article applies different approaches to distinguish state dependence from unobserved heterogeneity and serial correlation and, hence, test for state dependence in consumer brand choices. First, we apply a simple method proposed by Chamberlain, which involves lagged exogenous variables only. Second, we also estimate a lagged-dependent-variable specification proposed by Wooldridge. Third, we use the estimation approach suggested by Wooldridge to estimate a model with both lagged dependent and exogenous variables to distinguish between the two different sources of choice dynamics, state dependence and lagged effects of the exogenous variables. Our analysis reveals that the best approach is to use models with both lagged dependent and exogenous variables. Our findings include strong evidence for state dependence in five out of the six product categories studied in this article.

The aim of this paper is to explore the links between brand equity, consumer learning and consumer choice processes in general and considering two recent trends in the market place: store brands and the Internet. We first review the advances that have occurred in brand equity research in marketing in the past decade, with particular emphasis on integrating the separate streams of research emanating from cognitive psychology and information economics. Brand equity has generally been defined as the incremental utility with which a brand endows a product, compared to its non-branded counterpart. We amplify this definition: we propose that brand equity be the incremental effect of the brand on all aspects of the consumer's evaluation and choice process. We propose an agenda of research based on this amplified definition.

Discrete choice models have been widely estimated on scanner panel data to study consumer choice. One challenge in scanner panel research is that only the prices of the items bought are recorded. The ad hoc models used to fill in the missing prices of non-purchased brands may create a self-selection bias in estimating consumer price sensitivities. This type of bias is also present in existing studies of coupon effects. To obtain consistent estimates of price elasticities in the presence of missing price and coupon values, we estimate a brand choice model jointly with models for the price and coupon processes.

Models of consumer learning and inventory behavior have both proven to be valuable for explaining consumer choice dynamics. In their pure form these models assume consumers solve complex dynamic programming (DP) problems to determine optimal choices. For this reason, these models are best viewed as “as if” approximations to consumer behavior. In this paper we present an estimation method, based on Geweke and Keane (2000), which allows us to estimate dynamic models without solving a DP problem and without strong assumptions about how consumers form expectations about the future. The relatively low computational burden of this method allows us to nest the learning and inventory models. We also incorporate the “price consideration” mechanism of Ching, Erdem and Keane (2009), which essentially says that consumers may not pay attention to a category in every period. The resulting model may be viewed as providing a more “realistic” or “descriptive” account of consumer choice behavior.

We construct two models of the behavior of consumers in an environment where there is uncertainty about brand attributes. In our models, both usage experience and advertising exposure give consumers noisy signals about brand attributes. Consumers use these signals to update their expectations of brand attributes in a Bayesian manner. The two models are (1) a dynamic model with immediate utility maximization, and (2) a dynamic “forward-looking” model in which consumers maximize the expected present value of utility over a planning horizon. Given this theoretical framework, we derive from the Bayesian learning framework how brand choice probabilities depend on past usage experience and advertising exposures. We then form likelihood functions for the models and estimate them on Nielsen scanner data for detergent. We find that the functional forms for experience and advertising effects that we derive from the Bayesian learning framework fit the data very well relative to flexible ad hoc functional forms such as exponential smoothing, and also perform better at out-of-sample prediction. Another finding is that in the context of consumer learning of product attributes, although the forward-looking model fits the data statistically better at conventional significance levels, both models produce similar parameter estimates and policy implications. Our estimates indicate that consumers are risk-averse with respect to variation in brand attributes, which discourages them from buying unfamiliar brands. Using the estimated behavioral models, we perform various scenario evaluations to find how changes in marketing strategy affect brand choice both in the short and long run. A key finding obtained from the policy experiments is that advertising intensity has only weak short run effects, but a strong cumulative effect in the long run. The substantive content of the paper is potentially of interest to academics in marketing, economics and decision sciences, as well as product managers, marketing research managers and analysts interested in studying the effectiveness of marketing mix strategies. Our paper will be of particular interest to those interested in the long run effects of advertising. Note that our estimation strategy requires us to specify explicit behavioral models of consumer choice behavior, derive the implied relationships among choice probabilities, past purchases and marketing mix variables, and then estimate the behavioral parameters of each model. Such an estimation strategy is referred to as “structural” estimation, and econometric models that are based explicitly on the consumer's maximization problem and whose parameters are parameters of the consumers' utility functions or of their constraints are referred to as “structural” models. A key benefit of the structural approach is its potential usefulness for policy evaluation. The parameters of structural models are invariant to policy, that is, they do not change due to a change in the policy. In contrast, the parameters of reduced form brand choice models are, in general, functions of marketing strategy variables (e.g., consumer response to price may depend on pricing policy). As a result, the predictions of reduced form models for the outcomes of policy experiments may be unreliable, because in making the prediction one must assume that the model parameters are unaffected by the policy change. Since the agents in our models choose among many alternative brands, their choice probabilities take the form of higher-order integrals. We employ Monte-Carlo methods to approximate these integrals and estimate our models using simulated maximum likelihood. Estimation of the dynamic forward-looking model also requires that a dynamic programming problem be solved in order to form the likelihood function. For this we use a new approximation method based on simulation and interpolation techniques. These estimation techniques may be of interest to researchers and policy makers in many fields where dynamic choice among discrete alternatives is important, such as marketing, decision sciences, labor and health economics, and industrial organization.

Internal market structure analysis infers brand positions in an attribute space from preference and choice data, given a market in which consumers have heterogeneous tastes for attributes. Previous market structure models have adopted a static framework (e.g., Elrod 1988, Chintagunta 1994, Elrod and Keane 1995). Furthermore, they assumed that consumer perceptions of brand attributes do not vary across consumers. Yet, these approaches may render inaccurate representations of market structure if there is state dependence in consumer choice behavior. This paper attempts to incorporate consumer choice dynamics into market structure models by specifying the source of choice dynamics explicitly. In particular, the process by which past purchases affect current choices is modeled in a framework which captures both consumer habit persistence and variety seeking behavior. More specifically, consumer preferences for brand attributes are modeled to depend on the attributes of brands bought on the previous purchase occasion. Furthermore, the modeling approach adopted incorporates heterogeneity in both consumer preferences and perceptions of brand attributes. The audience of this paper includes practitioners and academics interested in understanding consumer choice processes and inferring market structure from consumer choice data. The proposed models are estimated on Nielsen scanner panel data for margarine, peanut butter, yogurt, and liquid detergent using simulated maximum likelihood techniques. The empirical results suggest that accounting for choice dynamics improves both in-sample and out-of-sample fit. The results indicate that the average consumer is habit persistent in all the product categories studied. This result is consistent with the findings of Kannan and Sanchez (1994), who conducted an aggregate analysis of consumer variety seeking behavior across product categories. However, the results obtained in this paper suggest that consumers are heterogeneous with respect to the processes by which past purchases affect current purchases. These results provide strong evidence for habit persistence and variety seeking in brand attributes to be the behavioral source of consumer choice dynamics in food categories. Thus, consumer tastes (utility weights) seem to be affected by the attributes of the brands consumed in the past. Given the empirical result that a large proportion of consumers are habit persistent, this suggests that tastes are reinforced by the brand attributes consumed in the past. The empirical results also show that not accounting for state dependence in market structure models for panel data may produce misleading results, that is, depending on consumer behavior patterns, models that do not account for state dependence may distort the true nature of competition among brands. More specifically, the results confirm the expectation that if there is habit persistence, that is, if consumer tastes are reinforced by attributes of brands consumed in the past, models that do not capture this choice dynamics will overestimate the distance between (similar) brands. Furthermore, the policy experiments conducted suggest that (1) static models overestimate the short-run impact of a price cut on the sales of the brand on promotion, (2) price cuts hurt the sales of the more similar brands more, and (3) free samples affect relatively less similar brands the most. Finally, this paper studies variety seeking and habit persistence across brands over purchase occasions. However, variety seeking behavior may also involve the purchase of a portfolio of brands or items at a purchase occasion. Consumers may buy multiple items knowing that prior to the next trip they may want to consume different items (Simanson 1990, Walsh 1995). This type of behavior can be modeled within the context of dynamic expected utility maximization with forward-looking consumers. The development and estimation of market structure models that include forward-looking consumers who maximize expected-utility over a planning horizon, incorporating their future tastes and needs, and shopping for an inventory of brands, remain an important future research issue.

Building upon the ideas of information economics and market signalling theory, a formal conceptual framework for explaining the creation, management, transfer and measurement of brand equity has been proposed by Erdem and Louviere. The research presented in this paper is a first step in the operationalization of that framework by developing a method for the measurement of brand equity that is built upon a theory of consumer behavior. Specifically, designed choice experiments that account for brand name, product attributes, brand image and consumer heterogeneity effects are proposed as the method for quantifying a brand equity measure called the Equalization Price (EP). Given an existing market structure, brand images built over time by advertising and product experiences, consumer brand perceptions and preferences, EP is a measure of the implicit value to the individual consumer of the brand in a market in which some degree of differentiation exists vis-a-vis its implicit value in a market characterized by no brand differentiation. The proposed measure can be used for both existing products and proposed brand name extensions, so it can double as a product-concept screening tool. We illustrate the application of the method for measuring brand equity using a convenience sample of Canadian consumers. In each of three product classes (deodorants, jeans and athletic shoes) we defined five existing products and two brand name extensions; these were presented to the respondents in designed choice sets which systematically varied prices over a range actually found in the local market. Concurrently, information related to brand image, previous brand usage and consumer sociodemographic characteristics was collected. The consumer choice data were used to calibrate Multinominal Logit choice models, from which we derive EP estimates for our sample. We discuss the interpretation of the modelling results for our data, illustrating the types of managerially relevant information about brand equity and its transfer that can be obtained from the method.

The recent marketing literature reflects a growing interest in structural models, stemming from (1) the desire to test a variety of behavioral theories with market data, and (2) recent developments that facilitate estimation of and inference for these models. Whether one should always go through the effort of developing such tightly parameterized models with the associated computational burden of estimating them and whether it pays off to make strict behavioral assumptions in terms of better decisions remain open questions. To shed some light on these issues, we provide examples of structural approaches to consumer choice and demand as well as examples where the goal is to study the nature of competition in the marketplace. From that review comes our discussion of issues in the development and application of structural models, including their estimation, testing, and validation, their applicability in the practice of marketing, and their usefulness for normative as well as descriptive purposes.

This paper examines the effects of brand credibility, a central concept in information economics-based approaches to brand effects and brand equity, on consumer choice and choice set formation. We investigate the mechanisms through which credibility effects materialize, namely through perceived quality, perceived risk and information costs saved. The credibility of a brand as a signal is defined as the believability of the product position information contained in a brand, which depends on consumer perceptions of the willingness and ability of firms to deliver what they have promised. The choice set is defined as the collection of brands that have a non-zero probability of being chosen among those actually available for choice in a given context. Furthermore, we study the impact of brand credibility on the variance of the stochastic component of utility. Choice model parameters not only capture the impact of systematic utility differences on choice probabilities, but the magnitude of this systematic impact is moderated by the relative importance of the stochastic utility component in preference. We term this moderation phenomenon "preference discrimination," which we conceptualize as the decision makers' capacity to effectively discriminate between products' utilities in choice situations. We estimate a discrete choice model of brand choice set formation and preference discrimination on experimental data in two categories (juice and PC) and find strong evidence for brand credibility effects and differential mechanisms through which brand credibility's impact materializes on brand choice conditional on choice set, choice set formation and preference discrimination.

The recent marketing literature reflects a growing interest in structural models, stemming from: 1) the desire to test a variety of behavioral theories with market data, and 2) recent developments that facilitate estimation of and inference for these models. Whether one should always go through the effort of developing such tightly parameterized models with the associated computational burden of estimating them, and whether it pays off to make strict behavioral assumptions in terms of better decisions, remain open questions. To shed some light on these issues, we provide examples of structural approaches to consumer choice and demand as well as examples where the goal is to study the nature of competition in the marketplace. From that review spawns our discussion of issues in the development and application of structural models, including their estimation, testing and validation, their applicability in the practice of marketing, and their usefulness for normative, as well as descriptive purposes.