Knowledge that Transforms

Christopher Adams (“ The Sealed-Bid Abstraction in Online Auctions ”; “ Rejoinder—Causes and Implications to Some Bidders Not Conforming to the Sealed-Bid Abstraction ”) is a staff economist with the Federal Trade Commission (FTC). He has a Ph.D. in economics from the University of Wisconsin and a B.Comm (Hons) from the University of Melbourne. At the FTC, he has worked on mergers and antitrust cases in a number of industries including pharmaceuticals, real estate, software, and retail. Before joining the FTC, he taught at the University of Vermont. In 2007, he was awarded the FTC's Paul Rand Dixon Award for outstanding contributions to the Commission. Paulo Albuquerque (“ Online Demand Under Limited Consumer Search ”) is an assistant professor of marketing at the Simon Graduate School of Business, University of Rochester. He holds a Ph.D. in management from the UCLA Anderson School of Management. He is currently interested in competition and consumer behavior in online markets, new product diffusion across markets, and spatial competition models. Bart J. Bronnenberg (“ Online Demand Under Limited Consumer Search ”) is a professor of marketing and CentER research fellow at Tilburg University. He holds Ph.D. and M.Sc. degrees in management from INSEAD, Fontainebleau, France, and an M.S. in industrial engineering from Twente University, The Netherlands. He is currently interested in marketing strategy and multimarket competition in consumer goods and medical industries. He has previously worked, and continues to work, on empirical analyses of new product growth and consumer choice behavior. He was named the recipient of the 2003 and 2008 Paul Green Award, the 2003 International Journal of Research in Marketing (IJRM) Best Paper Award, and the 2004 John D. C. Little Best Paper Award. Anne T. Coughlan (“ Optimal Reverse Channel Structure for Consumer Product Returns ”) is the John L. and Helen Kellogg Professor of Marketing at the Kellogg School of Management at Northwestern University. Her research on channel design and compensation problems, sales force management, sales force compensation, and pricing has been published in the top journals for marketing and operation and decision technologies. She is an area editor for Marketing Science and an author of the Marketing Channels textbook. Her favorite leisure activity is cultivating cacti and succulents in her greenhouse. Brett Danaher (“ Converting Pirates Without Cannibalizing Purchasers: The Impact of Digital Distribution on Physical Sales and Internet Piracy ”) is an assistant professor of economics at Wellesley College. He received a bachelor's of science in economics from Haverford College and a Ph.D. in managerial science and applied economics from The Wharton School of the University of Pennsylvania. His research interests include digital media, intellectual property, and the economics of information goods. Marnik G. Dekimpe (“ Estimating Cannibalization Rates for Pioneering Innovations ”) is a research professor at Tilburg University (The Netherlands) and a professor of marketing at the Catholic University Leuven (Belgium), and he is currently an academic trustee with both MSI and AiMark. He received his Ph.D. from the University of California, Los Angeles. He has advised several key players in the consumer packaged goods industry, especially on private-label and marketing-mix effectiveness issues. He has won best paper awards at Marketing Science, the Journal of Marketing Research, the International Journal of Research in Marketing, and Technological Forecasting and Social Change. He has also won the 2010 Louis W. Stern Award for his work on the valuation of Internet channels. He serves as editor for the International Journal of Research in Marketing and serves on the editorial boards of Marketing Science, the Journal of Marketing, the Journal of Marketing Research, Marketing Letters, the Review of Marketing Science, and the Journal of Interactive Marketing. Samita Dhanasobhon (“ Converting Pirates Without Cannibalizing Purchasers: The Impact of Digital Distribution on Physical Sales and Internet Piracy ”) is a Ph.D. candidate in public policy and management at the Heinz College, Carnegie Mellon University. Her research interests include digital piracy, digital media, and e-commerce marketing. Peter S. Fader (“ Customer-Base Analysis in a Discrete-Time Noncontractual Setting ”) is the Frances and Pei-Yuan Chia Professor of Marketing at The Wharton School of the University of Pennsylvania, and codirector of the Wharton Interactive Media Initiative. Scott Fay (“ The Economics of Buyer Uncertainty: Advance Selling vs. Probabilistic Selling ”) is an assistant professor of marketing at the Whitman School of Management at Syracuse University. He received a Ph.D. in economics from the University of Michigan and previously taught at the Warrington College of Business of the University of Florida. In his research, he employs analytical modeling to study a variety of topics, many of which are related to e-commerce, including reverse auctions, opaque products, the personalization process, the bundling of information goods, shipping fee schedules, retail price endings, and consumer bankruptcy. He was elected to and served two terms as the newsletter editor for the INFORMS Society for Marketing Science (2002–2006). He serves on the editorial board of Marketing Science, served as a guest area editor for Marketing Science, and recently received the Meritorious Service Award from Management Science. Bruce G. S. Hardie (“ Customer-Base Analysis in a Discrete-Time Noncontractual Setting ”) is a professor of marketing at the London Business School. His primary research interest lies in the development of data-based models to support marketing analysts and decision makers, with a particular interest in models that are easy to implement. Most of his current projects focus on the development of probability models for customer-base analysis. Ernan Haruvy (“ Search and Choice in Online Consumer Auctions ”) is an associate professor of marketing at the University of Texas at Dallas. He received his Ph.D. in economics in 1999 from the University of Texas at Austin. His research focuses primarily on market design, with a special interest in auctions, procurement, learning, and bounded rationality. Gerald Häubl (“ Optimal Reverse-Pricing Mechanisms ”) is the Canada Research Chair in Behavioral Science and an associate professor of marketing at the University of Alberta's School of Business. He is the founding director of the Institute for Online Consumer Studies (IOCS). He received M.S. and Ph.D. degrees in business administration and marketing from the Vienna University of Economics and Business Administration (Wirtschaftsuniversität Wien) in his native Austria. His primary research interests are consumer decision making, the construction of preference and value, human–information interaction, decision assistance for consumers, and bidding behavior in interactive-pricing markets. Ali Hortaçsu (“ Commentary—Do Bids Equal Values on eBay? ”) is a professor of economics at the University of Chicago. He received his Ph.D. from Stanford University in 2001, and his main research area is industrial organization. He has developed novel econometric methods to study auction and matchmaking markets, and he has applied these methods to answer market design questions in central bank operations, government bond auctions, electricity markets, online auctions, and online matchmaking. He has also developed empirical methods to study markets with search frictions, with applications to e-commerce and the mutual fund industry. He has been awarded an Alfred P. Sloan Fellowship and an NSF CAREER grant and is a research associate of the National Bureau of Economic Research. He has served as the coeditor for the International Journal of Industrial Organization and as associate editor for the Journal of Business and Economic Statistics and the Journal of Industrial Economics. Sandy Jap (“ The Seeds of Dissolution: Discrepancy and Incoherence in Buyer–Supplier Exchange ”) is the Dean's Term Chair Professor of Marketing at the Goizueta Business School at Emory University. She is a graduate of the University of Florida (Go Gators!) and has served on the faculties of the Sloan School of Management at the Massachusetts Institute of Technology and The Wharton School of the University of Pennsylvania. Her research interests lie in interorganizational exchange management and the design and management of business-to-business markets with auction mechanisms. She is an area editor for the International Journal of Research in Marketing and an editorial board member of the Journal of Marketing Research and Marketing Letters. Ujwal Kayande (“ The Seeds of Dissolution: Discrepancy and Incoherence in Buyer–Supplier Exchange ”) is a professor of marketing in the Research School of Business at the Australian National University. He was previously on the faculty at the Smeal College of Business (Pennsylvania State University) and the Australian Graduate School of Management (University of New South Wales, Sydney). He obtained his Ph.D. from the University of Alberta. His current research focuses on developing quantitative models to understand marketplace behavior and the effect of marketing activity upon that behavior; additionally, he is interested in understanding the pathways by which quantitative models impact business practice. He is a recipient of the 1998 Don Lehmann Award from the American Marketing Association. Jun B. Kim (“ Online Demand Under Limited Consumer Search ”) is an assistant professor at the College of Management, Georgia Institu

Customer retention and customer churn are key metrics of interest to marketers, but little attention has been placed on linking the different reasons for which customers churn to their value to a contractual service provider. In this paper, we put forth a hierarchical competing-risk model to jointly model when customers choose to terminate their service and why. Some of these reasons for churn can be influenced by the firm e.g., service problems or price--value trade-offs, but others are uncontrollable e.g., customer relocation and death. Using this framework, we demonstrate that the impact of a firm's efforts to reduce customer churn for controllable reasons is mitigated by the prevalence of uncontrollable ones, resulting in a “damper effect” on the return from a firm's retention marketing efforts. We use data from a provider of land-based telecommunication services to demonstrate how the competing-risk model can be used to derive a measure of the incremental customer value that a firm can expect to accrue through its efforts to delay churn, taking this damper effect into account. In addition to varying across customers based on geodemographic information, the magnitude of the damper effect depends on a customer's tenure to date. We discuss how our framework can be used to tailor the firm's retention strategy to individual customers, both in terms of which customers to target and when retention efforts should be deployed.

Brand preferences and marketplace demand are a reflection of the importance of underlying needs of consumers and the efficacy of product attributes for delivering value. Dog owners, for example, may look to dog foods to provide specific benefits for their pets (e.g., shiny coats) that may not be available from current offerings. An analysis of consumer wants for these consumers would reveal weak demand for product attributes resulting from low efficacy, despite the presence of strong latent interest. The challenge in identifying such unmet demand is in distinguishing it from other reasons for weak preference, such as general noninterest in the category and heterogeneous tastes. We propose a model for separating out these effects within the context of conjoint analysis, and we demonstrate its value with data from a national survey of toothpaste preferences. Implications for product development and reformulation are explored.

The idea of hierarchical, sequential, or intermediate effects has long been posited in textbooks and academic literature. Hierarchical effects occur when relationships among variables are mediated through other variables. Challenges in studying hierarchical effects in marketing include the large number of items present in most commercial studies and the presence of heterogeneous relationships among the variables. Existing approaches have dealt with the large number of variables by employing a factor structure representation of the data and have used standard mixture distributions for representing different response segments. In this paper, we propose a Bayesian model for the analysis of hierarchical data using the actual response items and incorporating heterogeneity that better reflects consumer stages in a decision process. Cross-sectional data from a national brand-tracking study are used to illustrate our model, where we find empirical support for a hierarchical relationship among media recall, brand beliefs, and intended actions. We find these effects to be insignificant when measured with standard models and aggregate analyses. The proposed model is useful for understanding the influence of variables that lead to intermediate as opposed to direct effects on brand choice.

The use of a durable good is limited by both its physical life and usable life. For example, an electric-car battery can last for five years (physical life) or 100,000 miles (usable life), whichever comes first. We propose a framework for examining how a profit-maximizing firm might choose the usable life, physical life, and selling price of a durable good. The proposed framework considers differences in usage rates and product valuations by consumers and allows for the effects of technological constraints and product obsolescence on a product's usable and physical lives. Our main result characterizes a relationship between optimal price, cost elasticities, and opportunity costs associated with relaxing upper bounds on usable and physical lives. We describe conditions under which either usable life or physical life, or both, obtains its maximum possible values; examine why a firm might devote effort to relaxing nonbinding constraints on usable life or physical life; consider when price cuts might be accompanied with product improvements; and examine how a firm might be able to cross-subsidize product improvements.

This paper examines pairwise assortment similarities at U.S. supermarkets to understand how assortment composition and size are related to underlying factors that describe local store clientele, local competitive structure, and the retail outlets' characteristics. The top-selling items, which cumulatively make up 50% of sales, are sold at nearly every store, but other items are viewed as optional. We find that, within states, supermarkets owned by the same chain carry similar assortments and that the composition of their clientele and the presence of competing stores have effects on assortment similarity that are an order of magnitude smaller than ownership structure. In contrast, we find that, across states, supermarkets owned by the same chain do version their assortment. We explain this difference using extant work on the minimal efficient scale of supermarkets and on local demand effects. Furthermore, we investigate the distribution and role of regional brands. We find that regional brands are primarily distributed by small regional chains or independent stores. “Value” regional brands are primarily distributed by supermarket firms without store brands, whereas the distribution of “premium” regional brands is unrelated to the presence of store brands. We discuss our findings in the context of modeling assortment decisions and manufacturers designing distribution policies.

The U.S. pharmaceutical industry spent upwards of $18 billion on marketing drugs in 2005; detailing and drug sampling activities accounted for the bulk of this spending. To stay competitive, pharmaceutical managers need to maximize the return on these marketing investments by determining which physicians to target as well as when and how to target them. In this paper, we present a two-stage approach for dynamically allocating detailing and sampling activities across physicians to maximize long-run profitability. In the first stage, we estimate a hierarchical Bayesian, nonhomogeneous hidden Markov model to assess the short- and long-term effects of pharmaceutical marketing activities. The model captures physicians' heterogeneity and dynamics in prescription behavior. In the second stage, we formulate a partially observable Markov decision process that integrates over the posterior distribution of the hidden Markov model parameters to derive a dynamic marketing resource allocation policy across physicians. We apply the proposed approach in the context of a new drug introduction by a major pharmaceutical firm. We identify three prescription-behavior states, a high degree of physicians' dynamics, and substantial long-term effects for detailing and sampling. We find that detailing is most effective as an acquisition tool, whereas sampling is most effective as a retention tool. The optimization results suggest that the firm could increase its profits substantially while decreasing its marketing spending. Our suggested framework provides important implications for dynamically managing customers and maximizing long-run profitability.

Two-sided exchange networks (such as eBay.com) often advertise their number of users, presumably to encourage further participation. However, these networks differ markedly on how they advertise their user base. Some highlight the number of sellers, some emphasize the number of buyers, and others disclose both. We use field experiment data from a business-to-business website to examine the efficacy of these different display formats. Before each potential seller posted a listing, the website randomized whether to display the number of buyers and/or sellers, and if so, how many buyers and/or sellers to claim. We find that when information about both buyers and sellers is displayed, a large number of sellers deters further seller listings. However, this deterrence effect disappears when only the number of sellers is presented. Similarly, a large number of buyers is more likely to attract new listings when it is displayed together with the number of sellers. These results suggest the presence of indirect network externalities, whereby a seller prefers markets with many other sellers because they help attract more buyers.

Many firms increasingly offer community venues to their customers to facilitate social interactions amongst them. Prior studies have shown that community participants have high engagement and loyalty toward the firm and provide useful feedback and referrals. However, it is not clear whether community participants are the firm's “fans” to begin with and self-select themselves into the community, or whether community participation leads to increased relational customer behaviors. In the current research, we employ data from a field experiment to help answer this question. The data come from a year-long study conducted by eBay Germany, and they reveal that a simple e-mail invitation significantly increased customer participation in the firm's community. Results also show that community participation had mixed effects on customers' likelihoods of participating in buying and selling behaviors. Community participation did not translate into increased behaviors, as would be commonly expected. Although there is no impact of participation on the number of bids placed or the revenue earned, there is a negative impact of participation on the number of listings and the amount spent. Together, these results suggest that the community participants become more selective and efficient sellers, and they also become more conservative in their spending on the items for which they bid. The results also show that customer community marketing programs may be targeted to a broader set of the firm's customers than just the fans.