Marketing Science

Trade shows are an important but under-researched component of the promotion mix for most industrial products. In this paper, we develop a three-stage model of trade show performance, relying on different indices of performance at each stage: attraction, contact, and conversion efficiency. We model the impact of preshow promotion, booth space, use of attention-getting techniques, competition, number and training of booth salespeople on the extent of attraction, contact, and conversion. The results from an empirical application using data from 85 firms that participated in a major trade show in 1991 suggest significant and different impact of these variables. In addition, we illustrate how the model can be used to evaluate trade-offs among different decision variables. Finally, we develop some general results implied by our model concerning the optimal allocation of trade show resources.

In this note, we explore channel interactions in an information-intensive environment where the retailer can implement personalized pricing and the manufacturer can leverage both personalized pricing and entry into a direct distribution channel. We study whether a retailer can benefit from personalized pricing and how upstream personalized pricing or entry into a direct distribution channel affects the allocation of channel profit. We find that the retailer is worse off because of its own or upstream personalized pricing, even when the retailer is a monopoly. However, it may still be optimal for the retailer to embrace personalized pricing in order to reap the strategic benefit of deterring the manufacturer from selling direct and targeting end consumers.

In certain product categories, large discount retailers are known to offer shallower assortments than traditional retailers. In this paper, we investigate the competitive incentives for such assortment decisions and the implications for manufacturers' distribution strategies. Our results show that if one retailer has the channel power to determine its assortment first, then it can strategically reduce its assortment by carrying only the popular variety while simultaneously inducing the rival retailer to carry both the specialty and popular varieties. The rival retailer then bears higher assortment costs, which leads to relaxed price competition for the commonly carried popular variety. We also show that when the manufacturer has relative channel power, it chooses alternatively to distribute both product varieties through both retailers. Our analysis suggests, therefore, that when a retailer becomes dominant in the distribution channel, it facilitates retail segmentation into discount shops, carrying limited product lines, and specialty shops carrying wider assortments. We also illustrate how retailer power leading to strategic assortment reduction can lead to lower consumer surplus.

The growing dominance of large retailers has altered traditional channel incentives for manufacturers. In this paper, we present a theoretical model to illustrate a strategic manufacturer response to a dominant retailer. In our model, a dominant and a weak retailer compete for the sale of a single product supplied by a single manufacturer. The dominant retailer has the power to dictate the wholesale price, but the manufacturer sets the wholesale price for the weak retailer. The manufacturer also has partial ability to transfer demand between retailers. In the strategic manufacturer response, the manufacturer begins by raising the wholesale price for the weak retailer over that for the dominant retailer. This makes the weak retailer the high-margin channel. The manufacturer then transfers demand to the weak retailer by engaging in joint promotions and advertising. We then use this strategic response model to derive a testable hypothesis that may guide future research in determining the source of dominant retailers’ low prices.

The common wisdom is that a retailer suffers when its wholesale supplier encroaches on the retailer's operations by selling directly to final consumers. We demonstrate that the retailer can benefit from encroachment even when encroachment admits no synergies and does not facilitate product differentiation or price discrimination. The retailer benefits because encroachment induces the encroaching supplier to reduce the wholesale price in order not to diminish unduly the retailer's demand for the manufacturer's wholesale product. The lower wholesale price and increased downstream competition mitigate double marginalization problems and promote efficiency gains that can secure Pareto improvements.

The retail trade today is increasingly dominated by large, centrally managed “power retailers.” In this paper, we develop a channel model in the presence of a dominant retailer to examine how a manufacturer can best coordinate such a channel.

We show that such a channel can be coordinated to the benefit of the manufacturer through either quantity discounts or a menu of two-part tariffs. Both pricing mechanisms allow the manufacturer to charge different effective prices and extract different surpluses from the two different types of retailers, even though they both have the appearance of being “fair.” However, quantity discounts and two-part tariffs are not equally efficient from the manufacturer’s perspective as a channel coordination mechanism. Therefore, the manufacturer must judiciously select its channel coordination mechanism.

Our analysis also sheds light on the role of “street money” in channel coordination. We show that such a practice can arise from a manufacturer’s effort to mete out minimum incentives to engage the dominant retailer in channel coordination. From this perspective, we derive testable implications with regard to the practice of street money.

For marketers, television remains the most important advertising medium. This paper proposes a two-sided model of the television industry. We estimate viewer demand for programs on one side and advertiser demand for audiences on the other. The primary objective is to understand how each group's program usage influences the other group.

Four main conclusions emerge. First, viewers tend to be averse to advertising. When a highly rated network decreases its advertising time by 10%, our model predicts a median audience gain of about 25% (assuming no competitive reactions). Second, we find the price elasticity of advertising demand is −2.9, substantially more price elastic than 30 years ago.

Third, we compare our estimates of advertiser and viewer preferences for program characteristics to networks' observed program choices. Our results suggest that advertiser preferences influence network choices more strongly than viewer preferences. Viewers' two most preferred program genres, Action and News, account for just 16% of network program hours. Advertisers' two most preferred genres, Reality and Comedy, account for 47% of network program hours.

Fourth, we perform a counterfactual experiment in which some viewers gain access to a hypothetical advertisement avoidance technology. The results suggest that ad avoidance tends to increase equilibrium advertising quantities and decrease network revenues.

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.

This paper provides some empirical generalizations regarding how the relative prices of competing brands affect the cross-price effects among them. Particular focus is on the asymmetric price effect and the neighborhood price effect. The asymmetric price effect states that a price promotion by a higher-priced brand affects the market share of a lower-priced brand more so than the reverse. The neighborhood price effect states that brands that are closer to each other in price have larger cross-price effects than brands that are priced farther apart. The main objective of this paper is to test if these two effects are generalizable across product categories, and to assess which of these two effects is stronger.

While the neighborhood price effect has not been rigorously tested in past research, the asymmetric price effect has been validated by several researchers. However, these tests of asymmetric price effect have predominantly used elasticity as the measure of cross-price effect. The cross-price elasticity measures the percentage change in market share (or sales) of a brand for 1% change in price of a competing brand. We show that asymmetries in cross-price elasticities tend to favor the higher-priced brand simply because of scaling effects due to considering percentage changes. Furthermore, several researchers have used logit models to infer asymmetric patterns. We also show that inferring asymmetries from conventional logit models is incorrect.

To account for potential scaling effects, we consider the absolute cross-price effect defined as the change in market share (percentage) points of a target brand when a competing brand's price changes by one percent of the product category price. The advantage of this measure is that it is dimensionless (hence comparable across categories) and it avoids scaling effects. We show that in the logit model with arbitrary heterogeneity in brand preferences and price sensitivities, the absolute cross-price effect is symmetric.

We develop an econometric model for simultaneously estimating the asymmetric and neighborhood price effects and assess their relative strengths. We also estimate two alternate models that address the following questions: (i) If I were managing the ith highest priced brand, which brand do I impact the most by discounting and which brand hurts me the most through price discounts? (ii) Who hurts whom in National Brand vs. Store Brand competition?

Based on a meta-analysis of 1,060 cross-price effects on 280 brands from 19 different grocery product categories, we provide the following empirical generalizations:

1. The asymmetric price effect holds with cross-price elasticities, but tends to disappear with absolute cross-price effects.

2. The neighborhood price effect holds with both cross-price elasticities and absolute cross-price effects, and is significantly stronger than the asymmetric price effect on both measures of cross-price effects.

3. A brand is affected the most by discounts of its immediately higher-priced brand, followed closely by discounts of its immediately lower-priced brand.

4. National brands impact store brands more so than the reverse when the cross-effect is measured in elasticities, but the asymmetric effect does not hold with absolute effects. Store brands hurt and are, in turn, hurt the most by the lower-priced national brands that are adjacent in price to the store brands.

5. Cross-price effects are greater when there are fewer competing brands in the product category, and among brands in nonfood household products than among brands in food products.

The implications of these findings are discussed.

Direct marketing is witnessing explosive growth. As consumers increasingly purchase products from their homes, their ability to judge the quality of products they buy is significantly reduced. In this paper we study how money-back guarantees can signal product quality in such environments. We interpret product quality broadly to mean both the level of attributes promised as well as the firm's consistency in delivering on those promises. Key aspects of our formulation are the explicit consideration of transaction costs, and alternative signals of product quality. Transaction costs are the costs the seller or buyer faces when redeeming a money-back guarantee. We show that money-back guarantees signal quality by exploiting the higher probability of returns for a lower quality product, and the attendant higher transaction costs. However, if the seller's transaction costs are very large, then there are less costly ways to signal, namely charging a high price. We compare the signaling performance of (1) price, (2) price with uninformative advertising, and (3) price with a money-back guarantee. Whereas uninformative advertising does not work at all in our model, under certain conditions a money-back guarantee is necessary to signal, and under other conditions, a money-back guarantee is a useful supplement to price.