Knowledge that Transforms

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.