Bart J. Bronnenberg

Exploring the Implications of the Internet for Consumer Marketing

Past commentaries on the potential impact of the Internet on consumer marketing have typically failed to acknowledge that consumer markets are heterogeneous and complex and that the Internet is but one possible distribution, transaction, and communication channel in a world dominated by conventional retailing channels. This failure has led to excessively broad predictions regarding the effect of the Internet on the structure and performance of product and service markets. The objective of this article is to provide a framework for understanding possible impacts of the Internet on marketing to consumers. This is done by analyzing channel intermediary functions that can be performed on the Internet, suggesting classification schemes that clarify the potential impact of the Internet across different products and services, positioning the Internet against conventional retailing channels, and identifying similarities and differences that exist between them. The article concludes with a series of questions designed to stimulate the development of theory and strategy in the context of Internet-based marketing.

Database Paper---The IRI Marketing Data Set

This paper describes a new data set available to academic researchers at the following website: http://mktsci.pubs.informs.org . These data are comprised of store sales and consumer panel data for 30 product categories. The store sales data contain 5 years of product sales, pricing, and promotion data for all items sold in 47 U.S. markets. In two U.S. markets, the store level data are supplemented with panel-level purchase data and cover the entire population of stores. Further information is available regarding store characteristics in these markets. We address several potential applications of these data, as well as the access protocol. The data set described in this paper is maintained by IRI. Any fees charged by IRI for the distribution of the data set will be used for the continual maintenance and updating of the data. Scholarships to cover IRIs fees for those who need it are available through the INFORMS Society for Marketing Science ISMS. Please see the website above for further details.

A Spatiotemporal Analysis of the Global Diffusion of ISO 9000 and ISO 14000 Certification

We study the global diffusion of ISO 9000 and ISO 14000 certification using a network diffusion framework. We start by investigating the presence and nature of contagion effects by defining alternative cross-country networks and testing their relative strength. Second, we study how the rate of diffusion differs between the two standards and between early-and later-adopting countries. Third, we identify which countries had more influence on diffusion than others. Empirically, we build a diffusion model which includes several possible cross-country contagion effects and then use Bayesian methods for estimation and model selection. Using country by year data for 56 countries and nine years, we find that accounting for cross-country influences improves both the fit and the prediction accuracy of our models. However, the specific cross-country contagion mechanism is different across the two standards. Diffusion of ISO 9000 is driven primarily by geography and bilateral trade relations, whereas that of ISO 14000 is driven primarily by geography and cultural similarity. We also find that the diffusion rate of ISO standards is higher for later-adopting countries and for the later ISO 14000 standard. We discuss several implications of our findings for the global diffusion of management standards.

The Emergence of Market Structure in New Repeat-Purchase Categories: The Interplay of Market Share and Retailer Distribution

The authors study brand-share dynamics among competing brands in new repeat-purchase categories. In such new categories, market shares are strongly affected by retailer distribution decisions. Because a retailer that considers a brand for distribution can take into account the prior performance of that brand with other retailers, the success of a manufacturer in obtaining distribution can depend positively on its brands market share to date. This creates positive feedback between a brands market share and its distribution over the growth stage of the category. Temporary positive feedback, along with the way manufacturers influence their brands market share and distribution, is hypothesized to drive the emergence of the market structure. The authors model this feedback to quantify the evolution of a brands coupled market share and distribution. Empirical results using data from the U.S. ready-to-drink tea category suggest that positive feedback between market share and distribution exists in the early growth stage of the category. Therefore, early in the life of the new category small short-term changes in market share or distribution may generate larger long-term changes in market share and distribution. Later, such momentum appears absent. In this context, the authors discuss how a late entrant fails to capture a sizeable share of the market.

Online Demand Under Limited Consumer Search

Using aggregate product search data from Amazon.com, we jointly estimate consumer information search and online demand for consumer durable goods. To estimate the demand and search primitives, we introduce an optimal sequential search process into a model of choice and treat the observed market-level product search data as aggregations of individual-level optimal search sequences. The model builds on the dynamic programming framework by Weitzman [Weitzman, M. L. 1979. Optimal search for the best alternative. Econometrica47(3) 641--654] and combines it with a choice model. It can accommodate highly complex demand patterns at the market level. At the individual level, the model has a number of attractive properties in estimation, including closed-form expressions for the probability distribution of alternative sets of searched goods and breaking the curse of dimensionality. Using numerical experiments, we verify the models ability to identify the heterogeneous consumer tastes and search costs from product search data. Empirically, the model is applied to the online market for camcorders and is used to answer manufacturer questions about market structure and competition and to address policy-maker issues about the effect of selectively lowered search costs on consumer surplus outcomes. We demonstrate that the demand estimates from our search model predict the actual product sales ranks. We find that consumer search for camcorders at Amazon.com is typically limited to 10--15 choice options and that this affects estimates of own and cross elasticities. In a policy simulation, we also find that the vast majority of the households benefit from Amazon.coms product recommendations via lower search costs.

Advertising versus pay-per-view in electronic media

Abstract Media providers frequently have to trade-off revenues from advertisers and subscribers. However, with contemporary electronic media, such as Internet websites, there exists the possibility of giving viewers of the same program the option to pay a higher price and view fewer advertisements, or pay a lower price but view more advertisements. With heterogeneous consumers, there will be some takers for both options, thereby allowing the media provider to derive the advantages of both subscription and advertising revenues. In this paper, we examine the number of options, the subscription price and the amount of advertising that should be offered to consumers. We find conditions where a pure advertiser-supported strategy or a pure pay-per-view strategy can be optimal. However, except under specified conditions, the optimal strategy is to charge a subscription price and have advertisements, but offer options to consumers.

Retrieving Unobserved Consideration Sets from Household Panel Data

The authors propose a new model to capture unobserved consideration from discrete choice data. This approach allows for unobserved dependence in consideration among brands, easily copes with many brands, and accommodates different effects of the marketing mix on consideration and choice as well as unobserved consumer heterogeneity in both processes. An important goal of this study is to establish the validity of the existing practice to infer consideration sets from observed choices in panel data. The authors show with experimental data that underlying consideration sets can be reliably retrieved from choice data alone and that consideration is positively affected by display and shelf space. Next, the model is applied to Information Resources Inc. panel data. The findings suggest that promotion effects are larger when they are included in the consideration stage of the two-stage model than in a single-stage model. The authors also find that consideration covaries across brands and that this covariation is mainly driven by unobserved consumer heterogeneity. Finally, the authors show the implications of the model for promotion planning relative to a more standard model of choice.

Using Multimarket Data to Predict Brand Performance in Markets for Which No or Poor Data Exist

The authors show how multimarket data can be used to make predictions about brand performance in markets for which no or poor data exist. To obtain these predictions, the authors propose a model for market similarity that incorporates the structure of the U.S. retailing industry and the geographic location of markets. The model makes use of the idea that if two markets have the same retailers or are located close to each other, then branded goods in these markets should have similar sales performance (other factors being held constant). In holdout samples, the proposed spatial prediction method improves greatly on naive predictors such as global-market averages, nearest neighbor predictors, or local averages. In addition, the authors show that the spatial model gives more plausible estimates of price elasticities. It does so for two reasons. First, the spatial model helps solve an omitted variables problem by allowing for unobserved factors with a cross-market structure. An example of such unobserved factors is the shelf-space allocations made at the retail-chain level. Second, the model deals with uninformative estimates of price elasticities by drawing them toward their local averages. The authors discuss other substantive issues as well as future research.

Brand history, geography and the persistence of brand shares

We document evidence of a persistent “early entry” advantage for brands in 34 consumer packaged goods industries across the 50 largest U.S. cities. Current market shares are higher in markets closest to a brand’s historic city of origin than in those farthest. For six industries, we know the order of entry among the top brands in each of the markets. We find an early entry effect on a brand’s current market share and perceived quality across U.S. cities. The magnitude of this effect typically drives the rank order of market shares and perceived quality levels across cities.

Structural Modeling and Policy Simulation

A primary goal of research in marketing is to evaluate and recommend optimal policies for marketing actions, or “instruments” in the terminology of Franses (2005). In this respect, marketing is a very policy-oriented field, and it is ironic that so much published research skirts the issue of policy evaluation. Franses’s article draws much needed attention to the question of what sort of model is usable for policy simulation and evaluation. Our perspective on what constitutes a valid model for policy evaluation differs from Franses’s view, but we believe our view complements his in many important respects. We also strongly believe that marketing has much to contribute to the literature on structural modeling. We outline some of what we believe are the advantages for marketing scholars of using structural modeling for policy evaluations and some of the challenges presented by marketing problems. Franses focuses on a reduced-form sales response model in which the outcome variable (yt) is modeled conditional on marketing variables (xt). If customers anticipate future marketing actions and take these into account in responding to the environment at time t, an additional equation is appended to the system to describe the evolution of the xt variables. In Franses’s view, this system can be used for policy simulation if both the y and x equations have timeinvariant parameters. That is, the Lucas critique, which implies that parameters of reduced-form models change if the policy regime changes, does not apply. According to Franses, a model must pass standard diagnostics, possess good predictive properties, and exhibit parameter stability to be useful for policy simulation. We applaud the attention Franses is bringing to model diagnostics. We believe that structural work in both marketing and economics should pay close attention to the central features of the data. Increased use of model diagnostics will help ensure that structural models are capable of capturing these features. However, we do not believe that all the criteria proposed by Franses, such as out-of-sample validity and parameter stability, are either necessary or sufficient to render a model useful for policy simulation. Reduced-form models can pass all diagnostics, including out-of-sample validation, and still provide misleading predictions about the effects of policy changes. Reduced-form