Knowledge that Transforms

Prior behavioral research has suggested that advertising can influence a consumer's quality evaluation through informative and transformative effects. The informative effect acts directly to inform a consumer of product attributes and hence shapes her evaluations of brand quality. The transformative effect affects the consumer's evaluation of brand quality by enhancing her assessment of her subsequent consumption experience. In addition, advertising may influence a consumer's utility directly, even without providing any explicit information—this is the persuasive effect. In this paper, we propose a framework that formally models the processes through which all three effects of advertisements impact consumers' brand evaluations and their subsequent brand choice decisions. In particular, we model source credibility, confirmatory bias, and bounded rationality on the part of consumers, by appropriately modifying the standard Bayesian learning approach. Our model conforms closely to prior behavioral literature and the experimental findings therein. In our empirical analysis, we get significant estimates of both informative and transformative effects across brands. We find interesting temporal patterns across the effects; for instance, the importance of transformative effects seem to grow over time, while that of informative effects diminishes. Finally, we conduct policy experiments to examine the impact of increased ad intensity on advertising effects, as well as the role played by consumption ambiguity.

How can a standardized product survive in a mass-customized world? This requires understanding that consumers often experience problems predicting their future hedonic reactions to new experiences (such as custom products), leading to feelings of regret. This form of regret occurs not because the custom product differs from specifications, but because consumers miswanted the design they ordered. Our analytic model shows that regret aversion induces consumers to design custom products to reflect the attributes of the available standard products. Consequently, regret-averse consumers may choose the standard product rather than place a custom order. The number of available standard products, however, moderates both these effects. Two experiments empirically substantiate the key predictions of the analytical model: (a) the custom product's resemblance to the standard product grows with regret aversion associated with miswanting, (b) there exists a segment of “regretfully loyal consumers” for the standard product in a mass-customized world and it expands with regret aversion, (c) both the above effects are weakened by the presence of a second standard product, and (d) the custom product can increase its market share when the number of standard products increases.

Co-branding is often used by companies to reinforce the image of their brands. In this paper, we investigate the conditions under which a brand's image is reinforced or impaired as a result of co-branding, and the characteristics of a good partner for a firm considering co-branding for image reinforcement. We address these issues by conceptualizing attribute beliefs as two-dimensional constructs: The first dimension reflects the expected value of the attribute, while the second dimension reflects the degree of certainty about the attribute. We argue that these parameters are updated after consumers are exposed to a co-branding activity, and we develop an analytical model that incorporates these notions. An analysis of the model leads to several propositions, which we test in an experiment. Our findings indicate that it is not necessarily in a brand's best interest to choose an alliance partner that is of the highest performance possible. Moreover, we find that, while expected values of the brand attributes may improve as a result of co-branding, under certain conditions the uncertainty associated with the brands increases through the alliance. Implications for co-branding researchers and practitioners are discussed.

Existing evidence suggests that preferences are affected by whether a price is presented as one all-inclusive expense or partitioned into a set of mandatory charges. To explain this phenomenon, we introduce a new mechanism whereby price partitioning affects a consumer's perception of the secondary (i.e., nonfocal) benefits derived from a transaction. Four experiments support the hypothesis that a partitioned price increases the amount of attention paid to secondary attributes tagged with distinct price components. Characteristics of the offered secondary attributes such as their perceived value, relative importance, and evaluability can therefore determine whether price partitioning stimulates or hinders demand. Beyond its descriptive and prescriptive implications, this theory contributes to the emerging notion that pricing can transform, as well as capture, the utility of an offer.

In mature markets with competing firms, a common role for advertising is to shift consumer preferences towards the advertiser in a tug-of-war, with no effect on category demand. In this paper, we analyze the effect of such “combative” advertising on market power. We show that, depending on the nature of consumer response, combative advertising can reduce price competition to benefit competing firms. However, it can also lead to a procompetitive outcome where individual firms advertise to increase their own profitability, but collectively become worse off. This is because combative advertising can intensify price competition such that an “advertising war” leads to a “price war.” Similar to price competition, advertising competition can result in a prisoner's dilemma where all competing firms make less profit even when the effect of each firm's advertising is to enhance consumer preferences in its favor. Given such procompetitive effects, we further show that cost of combative advertising could be a blessing in disguise—higher unit cost of advertising resulting in lower equilibrium levels of advertising, leading to higher prices and profits. We conduct a laboratory experiment to investigate how combative advertising by competing brands influences consumer preferences. Our experimental analysis offers strong support for our conclusions.

We examine grocery shopping paths using the traveling salesman problem (TSP) as a normative frame of reference. We define the TSP-path for each shopper as the shortest path that connects all of his purchases. We then decompose the length of each observed path into three components: the length of the TSP-path, the additional distance because of order deviation (i.e., not following the TSP-order of category purchases), and the additional distance because of travel deviation (i.e., not following the shortest point-to-point route). We explore the relationship between these deviations and different aspects of in-store shopping/purchase behavior. Among other things, our results suggest that (1) a large proportion of trip length is because of travel deviation; (2) paths that deviate substantially from the TSP solution are associated with larger shopping baskets; (3) order deviation is strongly associated with purchase behavior, while travel deviation is not; and (4) shoppers with paths closer to the TSP solution tend to buy more from frequently purchased product categories.

Which marketing strategies are most effective for introducing new brands? This paper sheds light on this question by ascribing growth performance to firms' postlaunch marketing choices. We decompose the success of a new brand into its ultimate market potential and the rate at which it achieves this potential. To achieve this aim we formulate a Bayesian dynamic linear model (DLM) of repeat purchase diffusion wherein growth and market potential are directly linked to the new brand's long-term advertising, promotion, distribution, and product strategy. We perform the analysis on 225 new-brand introductions across 22 repeat-purchase product categories over five years to develop generalized findings about the correlates of new-brand success. We find that access to distribution breadth plays the greatest role in the success of a new brand, and that investments in distribution and product innovation lead to greater marginal increases in sales for new brands than either discounting, feature/display, or advertising. Moreover, distribution interacts with other strategies to enhance their effectiveness. These findings underscore the utility of extending marketing mix models of new-brand performance to include product and distribution decisions.

Brand salience—the extent to which a brand visually stands out from its competitors—is vital in competing on the shelf, yet is not easy to achieve in practice. This study proposes a methodology to determine the competitive salience of brands, based on a model of visual search and eye-movement recordings collected during a brand search experiment. We estimate brand salience at the point of purchase, based on perceptual features (color, luminance, edges) and how these are influenced by consumers' search goals. We show that the salience of brands has a pervasive effect on search performance, and is determined by two key components: The bottom-up component is due to in-store activity and package design. The top-down component is due to out-of-store marketing activities such as advertising. We show that about one-third of salience on the shelf is due to out-of-store and two-thirds due to in-store marketing. The proposed methodology for competitive salience analysis exposes the optimal visual differentiation level of a brand versus its competitors, and of each SKU versus the other SKUs of the same brand. The model of the visual search process and methodology for competitive salience analysis enable diagnostic analyses of the current levels of visual differentiation of brands and SKUs at the point of purchase, and provide directions for increasing these.

Achannel 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. This article was originally published in Marketing Science, Volume 2, Issue 3, pages 239–272, in 1983.

We develop a testing methodology that can be used to predict the performance of e-mail marketing campaigns in real time. We propose a split-hazard model that makes use of a time transformation (a concept we call virtual time) to allow for the estimation of straightforward parametric hazard functions and generate early predictions of an individual campaign's performance (as measured by open and click propensities). We apply this pretesting methodology to 25 e-mail campaigns and find that the method is able to produce in an hour and fifteen minutes estimates that are more accurate and more reliable than those that the traditional method (doubling time) produces after 14 hours. Other benefits of our method are that we make testing independent of the time of day and we produce meaningful confidence intervals. Thus, our methodology can be used not only for testing purposes, but also for live monitoring. The testing procedure is coupled with a formal decision theoretic framework to generate a sequential testing procedure useful for the real time evaluation of campaigns.