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Trajectories of attention capture the accumulation of brand utility during complex decision-making tasks. Thus, attention trajectories, as reflected in eye movements, predict the final brand choice of 85% of consumers before they implement it. Even when observing eye movements in only the first quarter of the decision process, attention already predicts brand choice much better (45%) than chance levels (20%). This superior prediction performance is due to a “double attention lift” for the chosen brand: The chosen brand receives progressively more attention toward the moment of choice, and more of this attention is devoted to integrating information about the brand rather than to comparing it with other options. In contrast, the currently owned brand grabs attention early in the task, and its attention gain persists for brand-loyal consumers and shifts for brand-switching consumers. A new attention and choice model used in tandem with the Bayesian K-fold cross-validation methodology on eye-tracking data from 325 representative consumers uncovered these attention trajectory effects. The findings contribute to closing important knowledge gaps in the attention and choice literature and have implications for marketing research and managerial practice.

BACKGROUND The increasing prevalence of methicillin-resistant Staphylococcus aureus (MRSA) and its resistance to multiple antibiotics has become a serious challenge since the early 2000s. Especially, community-acquired MRSA (CA-MRSA) infections that appear mainly as skin and soft tissue infections (SSTIs) tend to increase worldwide. The objective of this cross-sectional study was to evaluate the trends in the frequency of SSTIs due to community-acquired S. aureus among children. METHODS All children with SSTIs caused by culture positive community-acquired S. aureus during the period from 2013 to 2018 were included in this study. Data of the outpatients were collected from medical records. Annual alteration in frequencies of CA-MRSA and community-acquired methicillin-sensitive S. aureus (CAMSSA) were evaluated. RESULTS A total of 112 cases was evaluated. Of these, 35 (31.25%) were CA-MRSA. The rates of CA-MRSA had emerged from an increasing annual frequency of 9.5 cases per 10,000 SSTIs as of 2014 to 96.8 cases per 10,000 SSTIs in 2018. The ratio of cases with CA-MRSA to cases with CA-MSSA was 0 - 0.09 in two years of the study period and increased to a maximum ratio of 0.6 - 0.72 in the last two years. Consequently, the frequency of S. aureus in cases with SSTIs was significantly higher in 2016 - 2018 compared to the initial study period within the years of 2013-2015 [p < 0.001, relative risk increase: 7 (2.6-28.7) for CA-MRSA and p=0.002, relative risk increase: 2.1 (1.2-3.5)]. Cases with CA-MRSA increased approximately eight-fold during the six-year-study period. CONCLUSIONS The rates of CA-MRSA in SSTIs among children increased significantly compared to CA-MSSA. The clinical impact of this increase should be evaluated, especially in patients with SSTI who are unresponsive to empirical treatment.

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.

Customer return rate evolution—whether return rates decrease or increase over the course of the customer-firm relationship—is of great economic importance to retailers. Analyzing the complete purchase and return behavior of about 8,000 customers of an online fashion retailer over seven years, we show that the average customer’s return rate increases by 40% from their first to tenth purchase. We identify three effects that show how customers’ past behavior explains returns for their current purchase and ultimately return rate evolution: 1) A greater number of previously purchased items indicates a decrease in return rate. We call this the brand experience effect. 2) A greater share of previously returned items indicates an increase in return rate. We call this the return habit effect. 3) A greater number of past purchase occasions indicates an increase in return rate. We call this the relationship effect. For managers, return rates evolution has important implications. If managers at the focal retailer ignored customers’ return rate evolution, they would overestimate cumulative customer value after 10 purchases by about 40%.

This paper develops a search model in which both the information obtained during the search process and the information possessed by consumers prior to search influence their choices. We estimate our model on a data set from an experiment that has two novel features: (i) it contains information on consumers’ prior brand ownership, familiarity with each brand, and prior experience using different product features; and (ii) it uses eye-tracking methods to capture search behavior during the experiment. Using these data we show consumers are generally more likely to search and buy brands they own and are familiar with, highlighting the importance of their prior information. We then quantify the impact of prior information on consumer choices, as well as document the estimation bias arising when brand prior information is absent from the model. Finally, through a series of counterfactuals, we explore the managerial value of using consumers’ brand priors to construct personalized ranking-based recommendation systems.

We develop a structural dynamic demand model that examines how brand preferences evolve when consumers are uncertain about product quality and their needs change periodically. We allow for strategic sampling behavior of consumers under quality uncertainty and allow for strategic sampling to increase periodically as consumers’ needs change periodically. We differ from previous work on forward-looking consumer Bayesian learning by allowing for 1) spill-over learning effects across different versions of products or products in different product categories that share a brand name and 2) duration-dependence in utility for a specific version of a product or product class to capture systematic periodic changes in consumer utility and migration of consumers across product versions or classes. We also assess the evolution of price elasticities in markets where there is consumer quality uncertainty that diminishes over time as consumers get more experienced. We estimate our model using scanner data for the disposable diapers category and discuss the consumer behavior and managerial implications of our estimation and policy simulation results.

This paper provides a new and complementary explanation for a pricing puzzle for seasonal products, namely, counter-cyclical pricing, drawing on the category-expansion effects of price promotions. Our study emphasizes the seasonal fluctuation in promotion frequency rather than the change in aggregate mean price across seasons, which motivates most existing studies. We propose a rationale for the counter-cyclical price promotions: consumers are more likely to increase category demand in response to promotions during periods of high demand, causing seasonally varying promotion effects on category expansion. We show promotion effects are amplified during high-demand periods if the product category is subject to stockpiling and endogenous-consumption behavior; that is, consumption is a function of inventory. Using scanner-panel data on the canned soup category, we find households' purchase patterns are consistent with the endogenous consumption hypothesis. We investigate the seasonally varying promotion effects using the framework of a dynamic inventory model with forward-looking consumers (Erdem, Imai, and Keane, 2003; Hendel and Nevo, 2006) by allowing the consumption rate to be endogenous to household inventory and subject to the exogenous seasonal fluctuation in category preference. Our results indicate the long-run promotion effects are underestimated by 32% during periods of high demand if endogenous consumption is ignored, and that with endogenous consumption, the long-run promotion effects increase by 24% across seasons, implying a larger gain of promotions during high-demand periods, which is a motivation for the counter-cyclical price promotions.

Pediatric cancer patients have an increased risk of potentially life-threatening fungal infections such as Candida parapsilosis, associated with long-term CVADs. The Infectious Diseases Society of America (IDSA) guidelines on Candida catheter-related bloodstream infections recommend systemic antifungal therapy and catheter removal. In this study, we focused on our experience with antifungal failure due to totally implanted catheter-associated C. parapsilosis bloodstream infections. We investigated cases leading to port removal in pediatric malignancy patients and the associated patient outcomes. In the first phase of the study, a retrospective chart review was performed to collect patient information, including primary disease; time from hospitalization to port-related candidemia; antifungal drug choice; and the time at which port removal occurred. During the second phase, antifungal susceptibility tests for C. parapsilosis were performed in our microbiology laboratory. All patients had fevers and were neutropenic at the time of candidemia diagnosis. The mean duration between the first isolation of Candida parapsilosis from the port samples to the port removal was 9.75 ± 5.29 days for 11 patients. Patient fevers lasted for a mean time of 16.22 ± 6.51 days. The median recovery duration from fever after CVC removal was four days (range 2-12 days). The median duration for achieving negative blood cultures, following antifungal treatment was 18 days (range 10-27 days). Our data favored the removal of catheters in the presence of ongoing fever, as suggested by the guidelines, independent of the chosen antifungal treatment. Future studies with large samples are needed to evaluate the effects of catheter removal on mortality rates and patient outcomes.

The large penetration of store brands has been accelerated by a substantial increase of their availability across various categories. Although store brands have generated tremendous interest in the literature, little work has been done on umbrella branding strategies for store brands. We extend the previous work of Erdem (1998) and Erdem et al. (2004) by studying the learning spillover effects of umbrella brands across categories for both national and store brands. We apply the Multivariate Multinomial Probit Model of cross-category learning across five product categories to study differences across store versus national umbrella brands in three countries (i.e., the United States, the United Kingdom, and Spain). Our results indicate that cross-category learning effects exist between different product categories in consumer packaged goods for both store brands and national umbrella brands, although some of the categories in which correlated learning happens differ between the two. The degree of cross-category learning also varies across categories.

A large literature demonstrates the empirical importance of internal reference price effects. There are several theories regarding how and why these effects arise. We offer a simple test that distinguishes between the two leading theories based on economically rational behavior: price as a signal of quality and price as a predictor of future prices. Our test builds on differences in how past consumer purchases interact with internal reference prices. We first validate the reliability of our test by applying it to synthetic data. We then apply our test to purchases of ketchup and diapers and find: (1) quality signaling is the dominant mechanism behind reference price effects in both categories; (2) consistent with the quality-signaling theory, reference price effects diminish as various measures of consumer experience increase; but (3) in both categories there are many individuals for whom price-prediction effects dominate quality-signaling effects.

The workhorse brand choice models in marketing are the multinomial logit (MNL) and nested multinomial logit (NMNL). These models place strong restrictions on how brand share and purchase incidence price elasticities are related. In this paper, we propose a new model of brand choice, the “price consideration” (PC) model, that allows more flexibility in this relationship. In the PC model, consumers do not observe prices in each period. Every week, a consumer decides whether to consider a category. Only then does he/she look at prices and decide whether and what to buy. Using scanner data, we show the PC model fits much better than MNL or NMNL. Simulations reveal the reason: the PC model provides a vastly superior fit to inter-purchase spells.

This paper discusses the role of agents’ beliefs and their implications for the economic modeling of their behavior, in particular, their behavior over time. The paper also discusses the corresponding planning problems facing both firms and consumers in their current decision making. After a general discussion of the consumer and firm problem, we discuss recent examples of some of the emerging empirical literature on dynamic choice behavior in marketing.

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 tests how well the information economics view of brand equity explains consumer brand choice in countries that represent different cultural dimensions. In this empirical analysis, the authors use survey and experimental data on orange juice and personal computers collected from respondents in Brazil, Germany, India, Japan, Spain, Turkey, and the United States. The results provide strong empirical evidence across countries for the role of brands as signals of product positions. In addition, the positive effect of brand credibility on choice is greater for consumers who rate high on either collectivism or uncertainty avoidance. Credible brands provide more value to collectivist consumers because such consumers perceive these brands as being of higher quality (i.e., reinforcing group identity). Credible brands provide more value to high-uncertainty-avoidance consumers because such brands have lower perceived risk and information costs.

We explore issues in theory-driven choice modeling by focusing on partial-equilibrium models of dynamic structural demand with forward-looking decision-makers, full equilibrium models that integrate the supply side, integration of bounded rationality in dynamic structural models of choice and public policy implications of these models.

We examine the role of brand credibility (trustworthiness and expertise) on brand choice and consideration across multiple product categories that vary in regard to potential uncertainty about attributes and associated information acquisition costs and perceived risks of consumption. We find that brand credibility increases probability of inclusion of a brand in the consideration set, as well as brand choice conditional on consideration. We also find that although credibility impacts brand choice and consideration set formation more and through more constructs in contexts with high uncertainty and sensitivity to such uncertainty, credibility effects are present in all categories. Finally, our results indicate that trustworthiness, rather than expertise, affects consumer choices and brand consideration more.

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.

We develop a model of household demand for frequently purchased consumer goods that are branded, storable and subject to stochastic price fluctuations. Our framework accounts for how inventories and expectations of future prices affect current period purchase decisions. We estimate our model using scanner data for the ketchup category. Our results indicate that price expectations and the nature of the price process have important effects on demand elasticities. Long-run cross price elasticities of demand are more than twice as great as short-run cross price elasticities. Temporary price cuts (or “deals”) primarily generate purchase acceleration and category expansion, rather than brand switching.

The authors investigate and find evidence for advertising and sales promotion spillover effects for umbrella brands in frequently purchased packaged product categories. The authors also capture the impact of advertising (as well as use experience) on both utility mean and variance across two categories. They show that variance of the random component of utility declines over time on the basis of advertising (and use experience) in either category. This constitutes the first empirical evidence for the uncertainty-reducing role of advertising across categories for umbrella brands.

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.

Religion is a powerful force in many people’s lives, impacting decisions about life, death, and everything in between. It may be difficult, then, to imagine that something as seemingly innocuous as the usage of brand name products might influence individuals’ commitment to religion. However, we demonstrate across 6 studies that when brands are a highly salient tool for self-expression, individuals are less likely to report and demonstrate strong religious commitment. We suggest that a desire to maintain consistency among self-identities is one important driver of this relationship and find that the effect is mitigated when the perceived distance between brands and religious values is minimized.

The authors attempt to draw profiles of reference-price shoppers. Specifically, the authors study how selected factors that affect brand choice are correlated with consumer sensitivity to gains and losses with respect to internal reference prices. They also study the interaction between sociodemographics and gain and loss sensitivity. Furthermore, the authors analyze cross-category correlations in gain and loss sensitivity to shed light on their individual- and category-specific characters. In three categories, the results show significant heterogeneity in loss sensitivity among consumers and indicate that loss sensitivity is greater and more heterogeneous than gain sensitivity. Across categories, the results show that loss-sensitive shoppers are less influenced by past brand use and that both loss- and gain-sensitive shoppers more sensitive to price, display, and feature than the average consumer. Loss-sensitive households tend to be larger, and their heads are less likely to be fully employed, whereas gain-sensitive households have no clear demographic profile. The authors also discuss the limitations of latent-class models in profiling consumer segments and show how these problems are overcome using models with continuous, correlated multivariate distributions.

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.

Both the recent advances in econometric analysis of panel data in economics and the availability of scanner panel data in marketing have motivated marketing researchers to conduct internal analysis of market structure based on revealed preference data. This kind of analysis allows the analyst to infer brand positions in an attribute space from preference and choice data, given a market in which consumers have heterogeneous tastes for attributes. Extant market structure models based on disaggregate panel data are probabilistic models that provide spatial representations of market structure but if a few products share the same brand name across product categories, consumer perceptions of product locations sharing the same brand name may be interdependent. The main contribution of this paper is to present a probabilistic spatial panel data model that allows the positions of products sharing the same name to be correlated across product categories.

In this article, the author studies the processes by which consumers’ quality perceptions of a brand in a product category are affected by their experience with the same brand in a different category. The model proposed and estimated explicitly incorporates some of the basic consumer behavior premises of signaling theory of umbrella branding (Montgomery and Wernerfelt 1992; Wernerfelt 1988). The author provides a framework to analyze the impact of marketing mix strategies in one product category on quality perceptions, consumer perceived risk, and consumer choice behavior in a different category. The model is estimated on panel data for two oral hygiene products, toothpaste and toothbrushes, in which a subset of brands share the same brand name across the two product categories. The results show strong support for the consumer premises of the signaling theory of umbrella branding.

The US auto industry experienced some turbulent times during the last decade, especially during the 2006–2011 period. The objective of this paper is twofold. First, we want to apply the information economics theoretic framework (e.g., Erdem and Swait (J Consum Psychol 7 (April): 131–157, 1998)) of brand equity and management to assess the impact of the events during this period on consumer brand perceptions and brand consideration in this market. Second, prior information economics theoretic work utilized cross-sectional data (and, further, collected only from student subjects). By collecting two waves of data at widely separated times (2006 and 2011) from consumers who own one or more cars, we are able to test the temporal stability of the information economics framework. Our results indicate that the impact of the various shocks (e.g., bankruptcies, buyouts, product recalls, and the like) on consumer perceptions and behavior during this period can be captured and explained quite well by the information economics theoretic framework: the framework shows remarkable temporal stability. This suggests that tracking key constructs of the framework can lead to reliable forecasts of consumer reactions and serve as the basis for “real-time” brand management, in both crisis and non-crisis modes. Refereed/Peer-reviewed

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.

Research examining the process of individual decision making over time is briefly reviewed. We focus on two major areas of work in choice dynamics: research that has examined how current choices are influenced by the history of previous choices, and newer work examining how choices may be made to exploit expectations about options available in the future. A central theme of the survey is that if a general understanding of choice dynamics is to emerge, it will come through the development of boundedly-rational models of dynamic problem solving that lie on the interface between economics and psychology.

Learning models extend the traditional discrete choice framework by postulating that consumers have incomplete information about product attributes, and that they learn about these attributes over time. In this survey we describe the literature on learning models that has developed over the past 20 years, using the model of Erdem and Keane (1996) as a unifying framework. We described how subsequent work has extended their modeling framework, and applied learning models to a wide range of different products and markets. We argue that learning models have contributed greatly to our understanding of consumer behavior, in particular in enhancing our understanding of brand loyalty and long run advertising effects. We also discuss the limitations of existing learning models and discuss potential extensions. One key challenge is to disentangle learning as a source of dynamics from other key mechanisms that may generate choice dynamics (inventories, habit persistence, etc.). Another is to enhance identification of learning models by collecting and utilizing direct measures of signals, perceptions and expectations.

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.

Casual observation suggests that a mega-multi-mall, such as West Edmonton Mall (WEM) and the Mall of America, which combines a very large shopping centre with a theme park, can play an important role in generating urban tourism. In the course of a campaign directed at obtaining government subsidies, the developers of WEM claimed it ‘brings nine million tourists per year to Edmonton’ and that ‘Tourists attracted to WEM and Canada Fantasyland spent over $700 million in Edmonton alone last year’. Thus it is important for governments to be able to assess the validity of such claims, and to determine whether such malls really generate substantial externalities. But mega-multi-malls simultaneously service a number of consumer markets, making an assessment more complicated than for most other tourist attractions. Here the approach WEM used to support its claims is described, and then the various methodological issues which arise when trying to obtain valid estimates of the economic impact of an institution such as WEM are discussed. Following this assessment useful supplementary data are identified, the data used by WEM are adjusted to account for clear methodological weaknesses, and the supplementary and revised data are then combined to produce a more reasonable estimate of the true economic impact of WEM.

Panel data, both diary and scanner, have been analyzed by marketing scientists for over thirty years. One of the important uses of panel data is to better understand consumer behavior by developing and testing hypotheses using the revealed preference data rather than experimental data that uses only self-reported behavior or behavior in a simulated choice environment. The purpose of this paper is to suggest areas of research where panel data can be used to better understand the underlying behavior of the panel members.

Learning models extend the traditional discrete choice framework by postulating that consumers have incomplete information about product attributes, and that they learn about these attributes over time. In this survey we describe the literature on learning models that has developed over the past 20 years, using the model of Erdem and Keane (1996) as a unifying framework. We described how subsequent work has extended their modeling framework, and applied learning models to a wide range of different products and markets. We argue that learning models have contributed greatly to our understanding of consumer behavior, in particular in enhancing our understanding of brand loyalty and long run advertising effects. We also discuss the limitations of existing learning models and discuss potential extensions. One key challenge is to disentangle learning as a source of dynamics from other key mechanisms that may generate choice dynamics (inventories, habit persistence, etc.). Another is to enhance identification of learning models by collecting and utilizing direct measures of signals, perceptions and expectations.

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.

To measure the extent of incomplete information about brand qualities faced by consumers, recent research in marketing and economics has extended traditional static choice models to explicitly allow for consumer learning. These models tend to be complicated and make stringent assumptions such as Bayesian updating. In this paper, we provide a simpler alternative method to measure how much consumers know about the quality of quasi-durable products. Our key insight is that for products that depreciate over time and require repeated purchases, individuals’ observed inter-purchase spells provide another measure of brand qualities in terms of durability. This is simply because the higher the durability, the longer a product can last in general, and hence its observed inter-purchase spells should also be longer. Based on this argument, we propose an empirical framework to estimate both the perceived brand quality (based on revealed preference data) and brand durability (based on brand-specific inter-purchase spells) and apply it to a scanner panel dataset for diapers. Our estimates allow us to compare these two measures of qualities and infer the extent of incomplete information faced by parents. With our results, we can address questions such as: Do parents make the right choice in the diapers category? Can they save some money by switching from a national brand to a store brand or the other way around? How much savings can they get?

Quantitative models in marketing typically focus on the household as the unit of analysis while ignoring the individual family members' behavior and behavioral interactions among household members. However, knowledge of such intra-household behavioral interaction enables marketers to target their communications more effectively. In this paper, we propose a modeling framework to capture the intra-household behavioral interaction based on family members' actual consumption behavior over time. We develop a model to capture multiple agents' (more than two individuals') simultaneous choice decisions over more than two choice alternatives. This is extremely difficult with other previously developed modeling approaches. We apply the proposed model to a context of family member's television viewing, and simultaneously model whether TV is on, which type of programs is playing and which family member(s) is(are) watching. The proposed model allows us to estimate the individual's intrinsic preference and the extrinsic preference from a joint consumption with other members. These estimates allow us to test several alternative group decision-making heuristics that may operate in those joint consumption occasions and conduct managerially useful counterfactual simulations.