Marketing Science

A new model of consumer behavior is developed using a hybrid of cognitive psychology and microeconomics. The development of the model starts with the mental coding of combinations of gains and losses using the prospect theory value function. Then the evaluation of purchases is modeled using the new concept of “transaction utility.” The household budgeting process is also incorporated to complete the characterization of mental accounting. Several implications to marketing, particularly in the area of pricing, are developed.

Branding has emerged as a top management priority in the last decade due to the growing realization that brands are one of the most valuable intangible assets that firms have. Driven in part by this intense industry interest, academic researchers have explored a number of different brand-related topics in recent years, generating scores of papers, articles, research reports, and books. This paper identifies some of the influential work in the branding area, highlighting what has been learned from an academic perspective on important topics such as brand positioning, brand integration, brand-equity measurement, brand growth, and brand management. The paper also outlines some gaps that exist in the research of branding and brand equity and formulates a series of related research questions. Choice modeling implications of the branding concept and the challenges of incorporating main and interaction effects of branding as well as the impact of competition are discussed.

Despite the explosive growth of electronic commerce and the rapidly increasing number of consumers who use interactive media (such as the World Wide Web) for prepurchase information search and online shopping, very little is known about how consumers make purchase decisions in such settings. A unique characteristic of online shopping environments is that they allow vendors to create retail interfaces with highly interactive features. One desirable form of interactivity from a consumer perspective is the implementation of sophisticated tools to assist shoppers in their purchase decisions by customizing the electronic shopping environment to their individual preferences. The availability of such tools, which we refer to as interactive decision aids for consumers, may lead to a transformation of the way in which shoppers search for product information and make purchase decisions. The primary objective of this paper is to investigate the nature of the effects that interactive decision aids may have on consumer decision making in online shopping environments.

While making purchase decisions, consumers are often unable to evaluate all available alternatives in great depth and, thus, tend to use two-stage processes to reach their decisions. At the first stage, consumers typically screen a large set of available products and identify a subset of the most promising alternatives. Subsequently, they evaluate the latter in more depth, perform relative comparisons across products on important attributes, and make a purchase decision. Given the different tasks to be performed in such a two-stage process, interactive tools that provide support to consumers in the following respects are particularly valuable: (1) the initial screening of available products to determine which ones are worth considering further, and (2) the in-depth comparison of selected products before making the actual purchase decision. This paper examines the effects of two decision aids, each designed to assist consumers in performing one of the above tasks, on purchase decision making in an online store.

The first interactive tool, a recommendation agent (RA), allows consumers to more efficiently screen the (potentially very large) set of alternatives available in an online shopping environment. Based on self-explicated information about a consumer's own utility function (attribute importance weights and minimum acceptable attribute levels), the RA generates a personalized list of recommended alternatives. The second decision aid, a comparison matrix (CM), is designed to help consumers make in-depth comparisons among selected alternatives. The CM allows consumers to organize attribute information about multiple products in an alternatives × attributes matrix and to have alternatives sorted by any attribute.

Based on theoretical and empirical work in marketing, judgment and decision making, psychology, and decision support systems, we develop a set of hypotheses pertaining to the effects of these two decision aids on various aspects of consumer decision making. In particular, we focus on how use of the RA and CM affects consumers' search for product information, the size and quality of their consideration sets, and the quality of their purchase decisions in an online shopping environment.

A controlled experiment using a simulated online store was conducted to test the hypotheses. The results indicate that both interactive decision aids have a substantial impact on consumer decision making. As predicted, use of the RA reduces consumers' search effort for product information, decreases the size but increases the quality of their consideration sets, and improves the quality of their purchase decisions. Use of the CM also leads to a decrease in the size but an increase in the quality of consumers' consideration sets, and has a favorable effect on some indicators of decision quality.

In sum, our findings suggest that interactive tools designed to assist consumers in the initial screening of available alternatives and to facilitate in-depth comparisons among selected alternatives in an online shopping environment may have strong favorable effects on both the quality and the efficiency of purchase decisions—shoppers can make much better decisions while expending substantially less effort. This suggests that interactive decision aids have the potential to drastically transform the way in which consumers search for product information and make purchase decisions.

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.

This paper surveys economic choice theory, stressing developments that permit use of data from psychometric and conjoint experiments to produce market demand forecasts. Alternatives to the widely used multinomial logit model are summarized, and a new method for estimating multinomial probits is described. An integration of choice models with attitudinal scaling and perceptual mapping, within a latent variable system, is described. Estimation of such systems under either “random effects” or “fixed effects” descriptions of heterogeneity across individuals is discussed. Issues in the use of choice models to describe responses from conjoint experiments are presented. New regression diagnostic tests for the consistency of multinomial logit representations are discussed.

Innovation is one of the most important issues in business research today. It has been studied in many independent research traditions. Our understanding and study of innovation can benefit from an integrative review of these research traditions. In so doing, we identify 16 topics relevant to marketing science, which we classify under five research fields:

Consumer response to innovation, including attempts to measure consumer innovativeness, models of new product growth, and recent ideas on network externalities; Organizations and innovation, which are increasingly important as product development becomes more complex and tools more effective but demanding;. Market entry strategies, which includes recent research on technology revolution, extensive marketing science research on strategies for entry, and issues of portfolio management; Prescriptive techniques for product development processes, which have been transformed through global pressures, increasingly accurate customer input, Web-based communication for dispersed and global product design, and new tools for dealing with complexity over time and across product lines; Defending against market entry and capturing the rewards of innovating, which includes extensive marketing science research on strategies of defense, managing through metrics, and rewards to entrants.

For each topic, we summarize key concepts and highlight research challenges. For prescriptive research topics, we also review current thinking and applications. For descriptive topics, we review key findings.

A channel of distribution consists of different channel members each having his own decision variables. However, each channel member's decisions do affect the other channel members' profits and, as a consequence, actions. A lack of coordination of these decisions can lead to undesirable consequences. For example, in the simple manufacturer-retailer-consumer channel, uncoordinated and independent channel members' decisions over margins result in a higher price paid by the consumer than if those decisions were coordinated. In addition, the ensuing suboptimal volume leads to lower profits for both the manufacturer and the retailer.

This paper explores the problems inherent in channel coordination. We address the following questions.

—What is the effect of channel coordination?

—What causes a lack of coordination in the channel?

—How difficult is it to achieve channel coordination?

—What mechanisms exist which can achieve channel coordination?

—What are the strengths and weaknesses of these mechanism?

—What is the role of nonprice variables (e.g., manufacturer advertising, retailer shelf-space) in coordination?

—Does the lack of coordination affect normative implications from in-store experimentation?

—Can quantity discounts be a coordination mechanism?

—Are some marketing practices actually disguised quantity discounts?

We review the literature and present a simple formulation illustrating the roots of the coordination problem. We then derive the form of the quantity discount schedule that results in optimum channel profits.

The PIMS (Profit Impact of Marketing Strategies) data entail sparse time-series observations for a large number of strategic business units (SBUs), In order to estimate disaggregate marketing mix elasticities of demand, a natural solution is to pool different SBUs. The traditional, a priori approach is to pool together those SBUs which one believes in advance to be very similar with respect to their marketing mix elasticities. We propose an alternative maximum likelihood, latent-pooling method for simultaneously pooling, estimating, and testing linear regression models empirically. This method enables the determination of a “fuzzy” pooling scheme, while directly estimating a set of marketing mix elasticities and intertemporal covariances for each pool of SBUs. Our analyses reveal different magnitudes and patterns of marketing mix elasticities for the derived pools. Pool membership is influenced by demand characteristics, business scope, and order of market entry.

In recent studies of channel competition, it has been found that channel intermediaries reduce the intensity of direct competition between manufacturers. The underlying channel structure in most studies consists of two manufacturers and two retailers each of whom sells only one manufacturer's product exclusively. This paper adds to this growing literature of channel competition by analyzing a channel structure with two competing manufacturers and one intermediary (a common retailer) that sells both manufacturers' products. Unlike some exclusive dealers or retail outlets of a manufacturer, however, a common retailer is often a powerful player in the market. This paper studies three noncooperative games of different power structures between the two manufacturers and the retailer, i.e., two Stackelberg and one Nash games. It is shown that some of the results depend critically on the form of the demand function. With a linear demand function, a manufacturer is better off by maintaining exclusive dealers while a retailer has an incentive to deal with several producers. All channel members as well as consumers are better off when no one dominates the market. The common retailer benefits more than the manufacturers do from a symmetric decrease in the manufacturing cost. As products are less differentiated, all channel members' prices and profits increase: a counterintuitive result. When the demand function is nonlinear, however, an exclusive dealer channel provides higher profits to all than a common retailer channel given a power structure. As products are more differentiated, a manufacturer's profit decreases when a common retailer is used, but increases when an exclusive dealer is used. These results underscore the importance of choosing a correct demand function for a channel decision.

This paper considers the pricing decision faced by a producer of a commodity with a short shelf or demand life. A hierarchical model is developed, and the results of the single period inventory model are used to examine possible pricing and return policies. The paper shows that several such policies currently in effect are suboptimal. These include those where the manufacturer offers retailers full credit for all unsold goods or where no returns of unsold goods are permitted. The paper also demonstrates that a policy whereby a manufacturer offers retailers full credit for a partial return of goods may achieve channel coordination, but that the optimal return allowance will be a function of retailer demand. Therefore, such a policy cannot be optimal in a multi-retailer environment. It is proven, however, that a pricing and return policy in which a manufacturer offers retailers a partial credit for all unsold goods can achieve channel coordination in a multi-retailer environment.