Knowledge that Transforms

Understanding the lead-lag relationship between distribution and demand is an important and challenging issue for all marketers. It is particularly challenging in the movie industry, where the very short lifespan and decaying revenue and exhibition patterns of motion pictures means that the associated time series are short and nonstationary, rendering existing econometric methods unreliable. We propose an alternate method that uses state-space diagrams to determine lead-lag relationships. Straightforward to apply and interpret, it takes advantage of the eye’s ability to see patterns that algebra-based formulations cannot easily recognize. A number of validation tests are provided to illustrate the usefulness and limitations of the method. We study the weekly data for 231 major movies released in 2000–2001. While econometric methods do not provide consistent results, the graphical method of visually inferred causality clearly shows a pattern that demand leads distribution for most movies. In other words, the dominant industry pattern is one of movie exhibitors monitoring box office sales and then responding with screen allocation decisions. The managerial implications of these findings are discussed.

We show that when a single media content distributor (such as a television cable company or an Internet provider) delivers advertising messages on behalf of multiple competing brands, it can sometimes utilize customized advertising to implement monopoly pricing. Even though such monopolistic pricing can be implemented with varying degrees of customization of commercials, product revenues and consumer surplus are highest when the distributor chooses the highest level of customization feasible. Consumers would, obviously, prefer aggressive price competition in product markets. However, given that collusion on prices is facilitated anyway, when the distributor acts as a common agent, the welfare of consumers is enhanced when commercials are better aligned with their preferences.

This research examines whether a low-ranking member in a high-status category (e.g., a low-end model of a high-end brand) or a high-ranking member in a low-status category (e.g., a high-end model of a low-end brand) is favored, holding the objective qualities of the items constant. Brand equity research suggests that the quality of a brand is more important than the ranking of a product within a brand. Our research documents a robust ranking effect—whereby a high-ranking product in a low-status category is favored over a low-ranking product in a high-status category even when information on competing categories is made available. We explain this effect in terms of narrow focusing and evaluability, and we identify boundary conditions of the effect.

We argue that standardized information disclosure (information using a common format and uniform metrics) creates asymmetric opportunities for firms, which affects their strategies and survival. We test our predictions using a longitudinal, quasi-experimental field study, involving the Nutrition Labeling and Education Act of 1990 (NLEA), and we focus on firm market share within a category as a key asymmetry. Findings indicate that, in general, the NLEA had no effect on firm responses. However, when accounting for firm differences, we observe that the NLEA led to (1) an increase in small-share firm exits and (2) a greater increase in distribution for large-share firms. No concurrent increase in price by large-share firms following the NLEA was observed. We conclude by discussing the implications of these effects for firm strategy, the design of public policy, and theories regarding the impact of information on markets.

In alliances jointly developing product and market, we first investigate how (a) the number of networks competing to develop a product, (b) the number of alternative technology platforms, and (c) market sensitivity to product development expenditures affect investments of partnering firms. We find that, in equilibrium, when the number of either competing networks or technologies increases, investments are more likely to be directed toward market, rather than product, development. Second, we consider the case in which firms continue to jointly develop a product but compete individually in the market. Our analysis suggests that forcing alliance partners to compete individually might not attenuate the underinvestment problem associated with new product alliances. Third, we extend the model to consider sequential market entry with rewards based on the order of entry, technology spillover, endogenous market size, and asymmetric technologies. Finally, key predictions of the basic model are tested in two experiments. The aggregate results provide strong support to the qualitative implications of the equilibrium solution but only mixed support to its quantitative predictions.

Delegating pricing decisions to the salesforce has been a salient issue for marketing academics and practitioners. We examine this issue in a competitive market using standard agency theory with symmetric and asymmetric information. Under symmetric information we find that the optimal contracts have a nice property that allows managers to reach the upper bound on firms’ profit by using either centralized or delegated pricing, and hence there are no incentive-based reasons to prefer centralized versus delegated contract types. Thus, managers can focus on the design of the optimal incentive scheme without worrying about the contract type. Under asymmetric information we find that there always exists an equilibrium where all firms use centralized pricing that is either unique or payoff equivalent to equilibria that have a combination of contract types. These results are robust to a large class of agent and market parameters. However, if restrictions are exogenously imposed on contract form or observability, the above results are no longer true, as in earlier work. We demonstrate our results by providing explicit solutions under the Holmstrom and Milgrom (1987) framework.

As the online channel matures, many firms are finding that an understanding of how their online customers' preferences and choices vary across geographical markets can be very useful. In this paper, we propose a spatial multinomial model of customer choice and illustrate how spatial modeling of choices of online customers across geographical markets provides useful insights in the context of a product mix and pricing decision of an online book publisher. The spatial multinomial model specifically accounts for the spatial correlations among customer choices among different product forms—print and PDF. The estimation results obtained using data generated from an online experiment show that the spatial model accounts for the geographical variation in many of the unobserved effects possibly due to locational differences and price sensitivities. The resultant spatial decision maps provide useful predictions as to how purchase rates vary across geographical markets as a function of the price differential between product forms, with implications for targeting customers through local market advertising, direct marketing, and cross-channel promotion.

We investigate differences in the dynamics of marketing decisions across geographic markets empirically. We begin with a linear-quadratic game involving forward-looking firms competing on prices and advertising. Based on the corresponding Markov perfect equilibrium, we propose estimable econometric equations for demand and marketing policy. Our model allows us to measure empirically the strategic response of competitors along with economic measures such as firm profitability. We use a rich dataset that combines sales, marketing mix, factor cost, and advertising cost data for eighteen geographic markets in the frozen entrée category. We find that larger markets tend to be less price-sensitive and more profitable than smaller markets. We also find evidence of positive carryover of own advertising on own demand. In terms of consumer substitution patterns, we find that the role of advertising (in our data) seems to be more category-building (complementary) than share-stealing (competitive). The complementary role is stronger in larger markets. On the supply side, we find that firms make smaller adjustments to own advertising as goodwill goes up. Consistent with cross-advertising effects on demand, firms make smaller (larger) adjustments to advertising in response to competitive goodwill in the less competitive larger (in the more competitive smaller) markets. Finally, we find that consumer welfare decreases (increases) in larger (smaller) markets when firms move to a zero-advertising regime.

In this research, we investigate the behavior of Cronbach’s coefficient alpha and its new standard error. We systematically analyze the effects of sample size, scale length, strength of item intercorrelations, and scale dimensionality. We demonstrate the beneficial effects of sample size on alpha’s standard error and of scale length and the strengths of item intercorrelations (effects that are substitutes in their benefits) on both alpha and its standard error. Our findings also speak to this adage: Heterogeneity within the item covariance matrix (e.g., through multidimensionality or poor items) negatively impacts reliability by decreasing the precision of the estimation. We also examined the question of “equilibrium” scale length, showing the conditions for which it is optimal to add no items, or one, or multiple items to a scale. In terms of “best practices,” we recommend that researchers report a confidence interval or standard error along with the coefficient alpha point estimate.

We study three determinants of the levels of price and detailing effort across geographic markets: the within-market response to each variable, the nature of interfirm strategic interactions both within that market and across markets. We empirically examine the interactions of Prozac, Zoloft, and Paxil across the United States, the United Kingdom, Germany, France, and Italy. Our results indicate that all three factors driving marketing mix interactions are at play in this product category. The U.S. market is less price-sensitive than the European markets. Detailing elasticities are comparable across the United States, Germany, and Italy, while the United Kingdom and France show greater elasticity. For the U.S. market, we find that almost all deviations from Nash pricing and detailing levels are due to within-market interactions. In the U.K. market, deviations from Nash prices come about due mostly to across-market interactions—both with the United States as well as the rest of Europe, whereas deviations from Nash detailing levels are mainly due to across-market interactions with the United States. For Italy, we observe that both within and across-market interactions affect price and detailing levels. Overall, the pattern of interactions makes observed prices more similar across countries than prices implied by the estimated elasticities. This underscores the importance of considering within- and across-market interactions in developing multimarket strategy.