Knowledge that Transforms

Marketing researchers and practitioners are interested in consumer loyalty because of its managerial consequences. Previous empirical studies find that consumers are loyal not only to a brand, but also to a firm (umbrella brand). That is, even when firms offer new products, consumers tend to continue to purchase from the same firm. This repeat-purchase behavior might result from state dependence or from heterogeneity. The meaning of state dependence is that the current choice behaviorally depends on the previous one. The traditional model of state dependence assumes that the previous choice affects the current utility. This study suggests another source of state dependence: The previous choice affects the current information set. Specifically, the model assumes that the consumer (a) knows the attributes of the new product offered by the firm from which he/ she purchased in the previous period, (b) is uncertain about the attributes of the new products offered by the other firms, (c) can obtain full information about the attributes of all the products through a costly search, and (d) if the consumer decides not to search, he/she purchases the new product offered by the firm from which he/she purchases in the previous period. It is shown that state dependence can result either from the effect of previous choices on the current utility or from its effect on the current information set. This theoretical result raises the following question: What kind of data does a researcher need in order to distinguish between the two sources of state dependence? This study shows that the two sources can be distinguished with a standard panel data set. In other words, although the new source of state dependence is based on the search activity of consumers, there is an identifying factor that enables a researcher to detect such activity even without direct data on search. The empirical distinction is possible because the behavioral implications of the two sources of state dependence are different. They differ in the effect of product attributes on the repeat-purchase probability. The following example partially illustrates this result: There are two firms A and B; the consumer purchased a product from firm A in period t – 1; the only product attribute is x; and the utility is a linear function of x. One aspect of our findings is that in the traditional model of state dependence a change in both x A and x B that leaves the difference between them, (x A – x B ), unchanged (neutral change, hereinafter) has no effect on the repeat-purchase probability. However, such a change does affect the repeat-purchase probability in the asymmetric information model of state dependence. This is only one aspect of the finding—the implications of the models differ in a more general fashion. The intuition of this result is the following. A neutral change has no effect on the repeat-purchase probability in the traditional model of state dependence, because it does not affect the difference between the utilities from both alternatives. In the asymmetric model of state dependence the consumer's decision process consists of two stages. First, he decides whether to search for information about the other alternative or not. Then, if he searches for information, he chooses the alternative that maximizes his utility. In the second stage, a neutral change has no effect on choices, since such a change does not affect the difference between the utilities from the two alternatives. In the first stage, the consumer knows x A , but does not know x B . It turns out that in this stage a neutral change does affect the search decision. When, for example, both x's decrease and the utility is a positive function of x, the probability of search increases, and thus the repeat-purchase probability decreases. The proposed source of state dependence is examined using structural estimation and panel data on television viewing choices in the United States. Controlling for both observed and unobserved heterogeneity, it is found that the suggested source is more important in creating repeat-purchase than the traditional one for most of the population (71%). This indicates that what was considered by previous studies to result from the dependence of consumer utility on their previous choices is at least partially due to the effect of the previous choices on consumers' information set. The distinction between the two sources of repeat-purchase is important because ignoring the informational explanation may lead to incorrect theoretical and empirical conclusions. For example, price discounts to induce trial are more important for consumers whose utility depends on previous choices, while advertising is more effective for those whose information set depends on previous choices.

Asymmetrically positioned retailers, who vary in the quality/in-store service offered, are increasingly using promotional advertising—the practice of advertising sale prices on familiar merchandise lines—to compete for customers who are willing to comparison shop. The objective of this paper is to examine the role of promotional advertising for stores that vary in their quality positioning in competing for customers using a game-theoretic model. Our focus is on two key retail promotional advertising decisions: the frequency with which to advertise price reductions and the accompanying depth of discount. We consider a stylized duopolistic retail market with the two stores that differ in their service positioning. We assume that each store enjoys a relative advantage in serving a subset or segment of customers who regularly visit it and whom we call “patrons” of the store. We assume that it costs more to shop at the less-frequented store. We further assume that consumers are only partially informed about the prevailing retail prices—while they perfectly know the posted price at the store that they patronize, they are uncertain about the price at the other store and have rational expectations about these prices. Consumers in this market differ on three dimensions: preference for service, shopping costs, and store switching costs. We explicitly consider two consumer segments differing in their willingness to pay for service. Furthermore, we assume store switching is more costly for the high-valuation segment. We allow for within-segment heterogeneity by assuming that consumers differ in their shopping costs. Our analysis shows that if promotional advertising is not “too costly,” the equilibrium strategies of the competing retailers entail occasionally posting its “regular” price but not advertising that price and on other occasions posting its “sale” price and advertising that price. The analysis also suggests that promotional advertising is driven by “offensive” (traffic-building) as well as “defensive” (consumer-retention) considerations. Furthermore, the relative importance of offensive and defensive considerations is influenced by the service positioning of the stores. Specifically, relative to the low-service store, promotional advertising by the high-service store is driven more by offensive consideration than defensive consideration. Finally, a store's service positioning impacts its frequency of promotional advertising and the depth of discount that it offers during “sale.” Specifically, relative to the low-service store, the high-service store offers advertised sales more frequently but with shallower discounts. These results follow from the fact that differences in service positioning lead to a natural consumer “self-selection.” Specifically, the consumer-mix of the high-service store comprises a higher fraction of the high-valuation consumers who are less sensitive to promotional advertising due to their higher store switching costs. Thus, if the low-service retailer were to build store traffic by targeting the customer mix of the high-service retailer (motivated by offensive consideration), it has to offer deeper discounts; yet the demand enhancement is lower. Thus, relative to the high-service store, promotional advertising is not that attractive for the low-service store. However, the low-service store still relies on offering discounted prices occasionally to retain its customer base. Thus when using promotional advertising to attract and retain customers, the high-service store should rely more on the “frequency cue,” while the low-quality store should rely more on the “magnitude cue.” We provide empirical support for the key predictions of our analytical model by collecting and analyzing retail promotional advertisements for stores that vary in their level of in-store service, published in major newspapers in a large U.S. metropolitan city. We collected data from 813 advertisements across 14 different product groups in the men’s and women's categories. The data are consistent with the model's predictions. Our theory and empirical analysis should be of interest to both academics and practitioners, particularly those in the area of channel management and promotional advertising.

A firm contemplating improvements to its product attributes would be interested in the dollar value the market attaches to any potential product modification. In this paper, we derive a measure of market value such that the comparison of the measure against the incremental unit cost of the attribute improvement is key in deciding whether or not the attribute improvement is profitable. Competition from other brands, the potential for market expansion, and heterogeneity in customer preference structures are explicitly modeled using the multinomial logit framework. The analysis yields a closed form expression for the market's value for an attribute improvement (MVAI). A key result we obtain is that customers should be differentially weighted based on their probability of purchasing the firm's product. In particular, customers who exhibit a very high or very low probability of choosing the firm's product should receive less weight in detemining MVAI. Because the probability of choice varies across products, the answer to the question of how much the market values an improvement depends on which firm is asking the question. It is shown that customers whose utilities have a greater random component should be weighted less. Furthermore, the measure developed is robust to the influence of outliers in the sample. An empirical illustration of the MVAI measure in the context of a new product development study is provided. The study illustrates the advantages of the proposed measure over currently used approaches and explores the possibility of competitive price reactions.

The Internet has led to a large number of third-party sources that offer high-quality information about firms's products at little or no cost to consumers. As a result, many of these sources have grown in popularity, extending well-beyond the usual reach of traditional third parties such as Consumer Reports and Kelly's Blue Book. For example, the online version of Edmunds offers, at no cost to consumers, information about new products, existing products, long-term tests, and buyers' guides, all relating to the automotive industry. AvWeb.com delivers weekly aviation news and new product reviews to its readers, and a large number of websites follow developments on computer platforms such as the Apple Macintosh. In this paper we analyze how the provision of third-party information affects the division of profits in a multiproduct distribution channel. To illustrate, consider the competition between Microsoft and Apple in the operating systems (OS) market and their channel relationship to CompUSA, a retailer that sells both Macs and Windows-based PCs. Consider two pieces of thirdparty information. First, suppose that CNET, an Internet technology site, reviews the newest upgrade of the MacOS and writes that the new user interface is even easier to use than previously. Second, suppose that an article in the technology section of the Wall Street Journal notes that changes in Apple's networking support now enable Macs to be better integrated into PC networks. These two pieces of information are similar in the sense that they both express good news about the MacOS and thus they both can be expected to benefit Apple by increasing consumer demand for Macs. One might also expect that in both cases CompUSA will capture some of the gains that come from the increased demand for Macs and that Microsoft will lose because the good news about the MacOS will induce some consumers to choose Macs over Windows-based PCs. However, we will show that this intuition is incorrect. The two reviews can have surprisingly different implications for the profits of Microsoft and CompUSA. The reason is that the two reviews differ on one crucial dimension: the group of customers for whom they are primarily relevant. The CNET review talks about improvements in the customer interface—precisely what Apple's core consumers care about. The Wall Street Journal review talks about compatibility with prevailing PC standards—important to consumers who care relatively more about compatibility and who are thus more likely to prefer Windows (Apple's noncore consumers). We show that good news about the MacOS that is more relevant to Apple's core consumers (the CNET review) benefits Microsoft but harms CompUSA, while good news about the MacOS that is more relevant to Apple's noncore consumers (the Wall Street Journal review) has the opposite effect. It harms Microsoft but benefits CompUSA. Stated more generally, our main result is that when third-party information affects consumers' product valuations, the type of information that induces the change is critical to understanding which firms gain and which firms lose. In particular, depending on the type of third-party information, we find that (1) a retailer can be harmed by good news about a product that it carries; (2) a manufacturer can gain from good news about a rival's product; and (3) good news about a product category need not benefit all the manufacturers in that category. There are three novel features of the analysis. First, we derive the equilibrium division of profit among firms when a retailer sells the products of competing manufacturers, and we have done so while placing few restrictions on the feasible set of contracts. Second, we show how this equilibrium division of profit lends itself to a simple graphical interpretation that depicts which firms gain and which firms lose from third-party information. Third, we provide a taxonomy of information types and identify the key features of each type that cause profit incentives to vary. In particular, we conceptualize information as having three components, namely (1) the products to which the information pertains, (2) whether the information is positive or negative, and (3) the consumers to whom the information is relevant. We show that all three information components play a role in determining the change in each firm's profit. Our framework can also be used to analyze a variety of other settings of interest; for example, it can be used to analyze profit incentives when the retailer has bargaining power, when there is downstream competition, and when there are non-information-based changes in consumers' valuations. In addition, our framework may be used both to analyze the effects on profits of persuasive advertising and to predict advertising content.

People consume products in a variety of environments. They drink beer, for example, by themselves, with close friends, on the beach, when playing cards, at tailgate parties, and while having dinner with their boss. Within these environments, an individual may prefer Schaefer beer when drinking alone, Budweiser when having a party, Corona when lying on the beach, and Heineken when dining out. Preferences change across environments because the benefits sought by the consumer change. Consumers may feel thirsty while lying on the beach, and they may want to display refined tastes while dining out. Moreover, the effect of environment may not be homogeneous, as some people enjoy meeting new people in social gatherings while others may prefer to visit with those who are more familiar. Even though consumers face the same objective environment, different motivating conditions and brand preferences may arise. It is important for marketing managers to understand how brand preferences change across people, environments, and motivating conditions and, more importantly, which product attributes are associated with these changes. Communication and positioning decisions are more likely to be effective if the relationships among objective environment, motivating conditions, and preferences for brand attributes are known. If motivating conditions are uniquely associated with individuals across environments, or with environments across individuals, then the basis of marketing analysis is at the individual or environmental level. If, however, motivating conditions arise from the intersection of individuals and their environments, then analysis conducted at the individual or environmental level will be insufficient to understand human behavior. In such a case, firms may want to view different environments as distinct markets, each with its own pattern of heterogeneous wants and competitive environment. In this paper, the influence of objective environments and motivating conditions on brand preference is investigated. The mathematical model is based on the economic framework of utility maximization and discrete choice, and it accommodates three challenges that arise in modeling variation in brand preference. First, consumer consideration sets and purchase histories can vary widely across individuals in a relevant universe. Because brand preferences are the dependent variables in our analysis, our method must be able to accommodate a large number of brands to avoid restricting its measured variation as the objective environment and motivating conditions change. We propose a method using partial ranking data, combined with pairwise trade-off data, to obtain estimates of brand preference for all brands in our study. Second, the model must allow for multiple effects, leading to both within-person and across-person heterogeneity in preferences. Variation in brand preference is investigated within a hierarchical Bayes model in which motivating conditions are related to brand preference through a regression model in the random effects specification. Third, it is often counterintuitive for respondents to express preferences for attribute combinations that do not actually exist. A statistical method model is proposed for decomposing aggregate brand preferences into preferences for core and extended product attributes. Data are collected from a national survey of consumer off-premises beer consumption. A total of 842 respondents from six different geographic markets participated. Data include preferred brand sets under different objective environments, brand choice rankings, product attributes, and motivating conditions. Effect sizes for respondent and objective environment are both large. We found that the level of explained variance in brand and attribute preference attributable to motivating conditions is greater than that accounted for by a simple interaction of respondent and environmental effects, suggesting that motivations provide a more sensitive description of variation in brand preference. Our findings indicate that 1) across individuals the objective environment is associated with heterogeneous, not homogeneous, motivating conditions; 2) within an individual, motivating conditions may change with variation in the objective environment; and 3) motivating conditions are related to preferences for specific attributes. Our results imply that the unit of analysis for marketing is properly a person-activity occasion. Brands, for example, are used in individual instances of behavior—a brand performs well or poorly on individual occasions of use. The relevant universe is enumerated in person-activity occasions rather than in respondents. For some activities, such as doing the laundry, the occasions may typically occur in relatively unchanging environments, and it may be appropriate to allow respondents to summarize over occasions of the activity. For other activities, such as snacking or drinking beer, the activity may occur in distinct kinds of environment. In the case of such activities, it is appropriate to allow for the effect of changing environments to manifest themselves, if present. Doing so may require sampling from the relevant universe of person-activity occasions over an appropriate time frame. The design must be such as to record intraindividual variability due to changes in the environment for action.

An interesting phenomenon has been the emergence of “infomediaries” in the form of Internet referral services in many markets. These services offer consumers the opportunity to get price quotes from enrolled brick-and-mortar retailers and direct consumer traffic to particular retailers who join them. This paper analyzes the effect of referral infomediaries on retail markets and examines the contractual arrangements that they should use in selling their services. We identify the conditions necessary for the infomediary to exist and explain how they would evolve with the growth of the Internet. The role of an infomediary as a price discrimination mechanism leads to lower online prices. Perhaps the most interesting result is that the referral infomediary can unravel (i.e., no retailer can get any net profit gain from joining) when its reach becomes too large. The analysis also shows why referral infomediaries would prefer to offer geographical exclusivity to joining retailers.

Many established industries, such as the online service industry, the telecommunication industry, or the fitness club industry, are access service industries. When using services in these industries, consumers pay for the privilege of accessing the firm's facilities but do not acquire any right to the facility itself. A firm's pricing decisions in access industries frequently come down to a simple choice among flat fee pricing, usage pricing, or two-part tariff pricing. However, it is not so simple for firms in those industries to make this choice. Access service firms typically face a mix of consumers who have intrinsically different usage rates. A key characteristic of access service firms, however, is that the cost of providing an additional minute of usage is typically negligible, as long as the firm has the necessary capacity to serve its customers. Service capacity, which corresponds to the total available time on a firm's system, is often limited. In this paper, we show that service capacity and consumer usage heterogeneity are two important factors that determine a firm's optimal choice. We develop a model that incorporates these two salient characteristics shared by access industries and study what determines a firm's choice among the three alternative pricing structures (flat fee pricing, usage pricing, or two-part tariff pricing). Our analysis shows that, in the presence of consumer usage heterogeneity, service capacity mediates a firm's optimal choice in a complex, yet predictable way. A firm's choice also hinges on whether heavy or light users are more valuable in terms of their willingness-to-pay on a per-unit-capacity basis. The presence of both consumer usage heterogeneity and capacity constraints prompts a firm to choose its pricing structure to attract a desired customer mix and to price discriminate. As a result, two-part tariff pricing is not always optimal in access industries, and a firm's pricing structure can vary in a complex way with the interaction of those two factors. Specifically, we show that when light users are more valuable, a firm may use a two-part tariff or a flat fee, depending on whether the firm is constrained by its service capacity, but never charge a usage price alone or offer any signing bonus (a negative flat fee or a flat payment to customers). When heavy users are more valuable, a firm may choose to set a usage price, a signing bonus plus a usage price, or flat fee. Interestingly, regardless of whether heavy or light users are more valuable in an access service industry, only flat rate pricing is a sustainable pricing structure once the industry has developed sufficient excess capacity. We also show that the optimal pricing strategy in access industries can have some intriguing, nonintuitive implications that have not been explored elsewhere. For instance, when the industry capacity is unevenly distributed between competing firms, the large-capacity firm may well be advised to increase, rather than to decrease, its price to accommodate the small firm. It would be too costly and too tactless for the large firm to do otherwise. In fact, the strategy of accommodation calls on the larger firm to retreat in both light and heavy user markets and leave more of its capacity idle and more of the market demand unmet when the small firm's capacity (hence, the industry capacity) increases. This implies that incremental policy measures that encourage the growth of smaller companies in the presence of a large company can be welfare-decreasing because the growth of smaller firm can force the retreat of a large company at the expense of market coverage. Today, services account for two-thirds to three-quarters of the GNP, not only in the United States but also in many industrial countries. Access industries are growing rapidly to exert profound impact on today's economy. However, service pricing in general and pricing access services in particular have not received adequate attention in the literature. In this paper, we take the first step in understanding how capacity constraints and consumer usage heterogeneity mediate the choice of pricing structures in both monopolistic and competitive contexts.

Marketers have long struggled with developing forecasts for new products before their launch. We focus on one data source—advance purchase orders—that has been available to retailers for many years but has rarely been tied together with postlaunch sales data. We put forth a duration model that incorporates the basic concepts of new product diffusion, using a mixture of two distributions: one representing the behavior of innovators (i.e., those who place advance orders) and one representing the behavior of followers (i.e., those who wait for the mass market to emerge). The resulting mixed-Weibull model specification can accommodate a wide variety of possible sales patterns. This flexibility is what makes the model well-suited for an experiential product category (e.g., movies, music, etc.) in which we frequently observe very different sales diffusion patterns, ranging from a rapid exponential decline (which is most typical) to a gradual buildup characteristic of “sleeper” products. We incorporate product-specific covariates and use hierarchical Bayes methods to link the two customer segments together while accommodating heterogeneity across products. We find that this model fits a variety of sales patterns far better than do a pair of benchmark models. More importantly, we demonstrate the ability to forecast new album sales before the actual launch of the album, based only on the pattern of advance orders.

The literature on cross-national diffusion models is gaining increased importance today due to the needs of present day managers. New product sales growth in a given nation or society is affected by many factors (Rogers 1995), and of these, sociocontagion (or word of mouth) has been found to be the most important factor that characterizes the diffusion process (Bass 1969, Moore 1995). Hence, it is interesting and perhaps challenging to analyze what would happen if a new product diffuses in parallel in two neighboring but culturally different countries. Not only will we expect the diffusion process in the two countries to be different, but we will also expect some interaction among them, especially if the two societies mingle with each other. There are two streams of research in cross-national diffusion. The first type focuses on exploring the differences between diffusion processes in two countries and finding out whether those differences can be attributed to social and cultural differences between the countries involved. Examples of this type of research are found in Takada and Jain (1991), Gatignon et al. (1989), Helsen et al. (1993), and Kumar et al. (1998). These studies did find some relationship between the cultural differences of the countries studied and the differences in the diffusion process. The second stream of research focuses on modeling explicitly the interaction between the diffusion processes in two countries. The interaction is typically captured through lead-lag effect (Eliashberg and Helsen 1996, Kalish et al. 1995), where the sales process in the lead country (i.e., the country where the product was first introduced) is modeled to affect the sales process in the lag country (i.e., the country where the product was introduced a few years later). Another method to study the interaction among the diffusion processes in two countries was suggested by Putsis et al. (1997), who used a “mixing model” to empirically explore the existence of such interactions. These studies basically observed that, when a new product is introduced early in one country and with a time lag in subsequent countries, the consumers in the lag countries learn about the product from the lead country adopters, resulting in a faster diffusion rate in the lag countries. Ganesh and Kumar (1996) formulized this effect as the learning effect and, subsequently, Ganesh et al. (1997) found this learning effect to be influenced by country-specific factors (cultural similarity, economic similarity, and time lag elapsed between the lead and the lag countries) and product-specific factors (continuous vs. discontinuous innovation and the presence or absence of a standardized technology). A careful analysis of the extant literature on the second stream of research would reveal that neither the learning effect model nor the mixing model can be modified to accommodate the other model. Our contribution to the literature exactly addresses this point. In this paper, an alternative framework is proposed that has two unique features. First, the framework is flexible enough to not only account for the lead country affecting the lag countries and vice versa, but also to accommodate the simultaneous interaction among countries in explaining the diffusion processes in the countries concerned. Using multiple product categories and a variety of new product introduction situations, we empirically demonstrate the flexibility and efficiency of our proposed framework. We found strong evidence of all types of interactions, namely, lead lag, lag lead, and simultaneous, which evidence suggests that one cannot afford to omit any of the interactions. The second unique feature of our paper is the estimation procedure that we used. Because statistical estimation of a dynamic process that includes lead-lag, lag-lead, and simultaneous types of causality within a single framework is not straightforward, we suggest an iterative estimation procedure for the estimation. This new procedure not only proved to be flexible in accommodating different types of interaction, but also converged rather quickly in all of the cases that we empirically tested. Noting that the statistical properties of these estimators are not generally available, we carried out a simulation exercise that clearly revealed the efficiency of the proposed estimation procedure. After analyzing the interaction, we went further and showed that the magnitude of the cross-national influences is affected by certain country-specific and product-specific factors. The flexibility of the proposed method over the existing methods is demonstrated through obtaining superior forecasts with the proposed method. Several interesting insights for managers concerned with formulating international marketing strategies are offered.