DECISION-MAKING UNDER UNCERTAINTY - CAPTURING DYNAMIC BRAND CHOICE PROCESSES IN TURBULENT CONSUMER-GOODS MARKETS

We construct two models of the behavior of consumers in an environment where there is uncertainty about brand attributes. In our models, bot h usage experience and advertising exposure give consumers noisy signa ls 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 ex pected present value of utility over a planning horizon. Given this th eoretical framework, we derive from the Bayesian learning framework ho w brand choice probabilities depend on past usage experience and adver tising 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 deriv e from the Bayesian learning framework fit the data very well relative to flexible ad hoc functional forms such as exponential smoothing, an d also perform better at out-of-sample prediction. Another finding is that in the context of consumer learning of product attributes, althou gh the forward-looking model fits the data statistically better at con ventional significance levels, both models produce similar parameter e stimates and policy implications. Our estimates indicate that consumer s are risk-averse with respect to variation in brand attributes, which discourages them from buying unfamiliar brands. Using the estimated b ehavioral models, we perform various scenario evaluations to find how changes in marketing strategy affect brand choice both in the short an d long run. A key finding obtained from the policy experiments is that advertising intensity has only weak short run effects, but a strong c umulative effect in the long Nn. The substantive content of the paper is potentially of interest to academics in marketing, economics and de cision sciences, as well as product managers, marketing research manag ers and analysts interested in studying the effectiveness of marketing mix strategies. Our paper will be of particular interest to those int erested in the long run effects of advertising. Note that our estimati on strategy requires us to specify explicit behavioral models of consu mer choice behavior, derive the implied relationships among choice pro babilities, past purchases and marketing mix variables, and then estim ate the behavioral parameters of each model. Such an estimation strate gy 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 pol icy evaluation. The parameters of structural models are invariant to p olicy, that is, they do not change due to a change in the policy. In c ontrast, the parameters of reduced form brand choice models are, in ge neral, functions of marketing strategy variables (e.g., consumer respo nse to price may depend on pricing policy). As a result, the predictio ns of reduced form models for the outcomes of policy experiments may b e unreliable, because in making the prediction one must assume that th e model parameters are unaffected by the policy change. Since the agen ts in our models choose among many alternative brands, their choice pr obabilities take the form of higher-order integrals. We employ Monte-C arlo methods to approximate these integrals and estimate our models us ing simulated maximum likelihood. Estimation of the dynamic forward-lo oking model also requires that a dynamic programming problem be solved in order to form the likelihood function. For this we use a new appro ximation method based on simulation and interpolation techniques. Thes e estimation techniques may be of interest to researchers and policy m akers in many fields where dynamic choice among discrete alternatives is important, such as marketing, decision sciences, labor and health e conomics, and industrial organization.