Inferring Market Structure from Customer Response to Competing and Complementary Products

We consider customer influences on market structure, arguing that market structure should explain the extent to which any given set of market offerings are substitutes or complements. We describe recent additions to the market structure analysis literature and identify promising directions for new research in market structure analysis. Impressive advances in data collection, statistical methodology and information technology provide unique opportunities for researchers to build market structure tools that can assist “real-time” marketing decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46981/1/11002_2004_Article_5088105.pd

Empirical Models of Manufacturer-Retailer Interaction: A Review and Agenda for Future Research

The nature of the interaction between manufacturers and retailers has received a great deal of empirical attention in the last 15 years. One major line of empirical research examines the balance of power between them and ranges from reduced form models quantifying aggregate profit and other related trends for manufacturers and retailers to structural models that test alternative forms of manufacturer-retailer pricing interaction. A second line of research addresses the sources of leverage for each party, e.g., trade promotions and their pass-through, customer information from loyalty programs, manufacturer advertising, productassortment in general, and private label assortment in particular. The purpose of this article is to synthesize what has been learnt about the nature of the interaction between manufacturers and retailers and the effectiveness of each party’s sources of leverage and to highlight gaps in our knowledge that future research should attempt to fill

Structural Models of Pricing

Handbook of Pricing Research in Marketing108-13

Structural Analysis of Manufacturer Pricing in the Presence of a Strategic Retailer

Consumer goods manufacturers usually sell their brands to consumers through common independent retailers. Theoretical research on such channel structures has analyzed the optimal behavior of channel members under alternative assumptions of manufacturer-retailer interaction (Vertical Strategic Interaction). Research in Empirical Industrial Organization has focused on analyzing the competitive interactions between manufacturers (Horizontal Strategic Interaction). Decision support systems have made various assumptions about retailer-pricing rules (e.g., constant markup, category-profit-maximization). The appropriateness of such assumptions about strategic behavior for any specific market, however, is an empirical question. This paper therefore empirically infers (1) the Vertical Strategic Interaction (VSI) between manufacturers and retailer, (2) the Horizontal Strategic Interaction (HSI) between manufacturers simultaneously with the VSI, and (3) the pricing rule used by a retailer. The approach is particularly appealing because it can be used with widely available scanner data, where there is no information on wholesale prices. Researchers usually have no access to wholesale prices. Even manufacturers, who have access to their own wholesale prices, usually have limited information on competitors' wholesale prices. In the absence of wholesale prices, we derive formulae for wholesale prices using game-theoretic solution techniques under the specific assumptions of vertical and horizontal strategic interaction and retailer-pricing rules. We then embed the formulae for wholesale prices into the estimation equations. While our empirical illustration is using scanner data without wholesale prices, the model itself can be applied when wholesale prices are available. Early research on the inference of HSI among manufacturers in setting wholesale prices using scanner data (e.g., Kadiyali et al. 1996, 1999) made the simplifying assumption that retailers charge a constant margin. This assumption enabled them to infer wholesale prices and analyze competitive interactions between manufacturers. In this paper, we show that this model is econometrically identical to a model that measures retail-price coordination across brands. Hence, the inferred cooperation among manufacturers could be exaggerated by the coordinated pricing (category management) done by the retailer. We find empirical support for this argument. This highlights the need to properly model and infer VSI simultaneously to accurately estimate the HSI when using data at the retail level. Functional forms of demand have been evaluated in terms of the fit of the model to sales data. But recent theoretical research on channels (Lee and Staelin 1997, Tyagi 1999) has shown that the functional form has serious implications for strategic behavior such as retail passthrough. While the logit and linear model implies equilibrium passthrough of less than 100% (Lee and Staelin call this Vertical Strategic Substitute (VSS)), the multiplicative model implies optimal passthrough of greater than 100% (Vertical Strategic Complement (VSC)). Because passthrough rates on promotions have been found to be below or above 100% (Chevalier and Curhan 1976, Armstrong 1991), we empirically test the appropriateness of the logit (VSS) and the multiplicative (VSC) functional form for the data. We perform our analysis in the yogurt and peanut butter categories for the two biggest stores in a local market. We found that the VSS implications of the logit fit the data better than the multiplicative model. We also find that for both categories, the best-fitting model is one in which (1) the retailer maximizes category profits, (2) the VSI is Manufacturer-Stackelberg, and (3) manufacturer pricing (HSI) is tacitly collusive. The fact that the retailer maximizes category profits is consistent with theoretical expectations. The inference that the VSI is Manufacturer-Stackelberg reflects the institutional reality of the timing of the game. Retailers set their retail prices after manufacturers set their wholesale prices. Note that in the stores and product categories that we analyze, the two manufacturers own the dominant brands with combined market shares of about 82% in the yogurt market and 65% in the peanut butter market. The result is also consistent with a balance of power argument in the literature. The finding that manufacturer pricing is tacitly collusive is consistent with the argument that firms involved in long-term competition in concentrated markets can achieve tacit collusion. Managers use decision support systems for promotion planning that routinely make assumptions about VSI, HSI, and the functional form. The results from our analysis are of substantive import in judging the appropriateness of assumptions made in such decision support systems.Structural Models, Horizontal Strategic Interaction, Vertical Strategic Interaction, Retailer Pricing, Promotional Planning, New Empirical Industrial Organization

Estimating Discrete Joint Probability Distributions for Demographic Characteristics at the Store Level Given Store Level Marginal Distributions and a City-Wide Joint Distribution

This paper provides a solution to the problem of estimating a joint distribution using the associated marginal distributions and a related joint distribution. The particular application we have in mind is estimating joint distributions of demographic characteristics corresponding to market areas for individual retail stores. Marginal distributions are generally available at the census tract level, but joint distributions are only available for Metropolitan Statistical Areas which are generally much larger than the market for a single retail store. Joint distributions over demographics are an important input into mixed logit demand models for aggregate data. Market shares that vary systematically with demographics are essential for relieving the restrictions imposed by the Independence from Irrelevant Alternative property of the logit model. We approach this problem by formulating a parametric function that incorporates both the city-wide joint distributional information and marginal information specific to the retail store’s market area. To estimate the function, we form moment conditions equating the moments of the parametric function to observed data, and we input these into a GMM objective. In one of our illustrations we use four marginal demographic distributions from each of eight stores in Dominick’s Finer Foods data archive to estimate a four dimensional joint distribution for each store. Our results show that our GMM approach produces estimated joint distributions that differ substantially from the product of marginal distributions and emit marginals that closely match the observed marginal distributions. Mixed logit demand estimates are also presented which show the estimates to be sensitive to the formulation of the demographics distribution. Copyright Springer Science + Business Media, Inc. 2005mixed logit, discrete joint probability distributions, generalized method of moments,