Knowledge that Transforms

Adib Bagh (“ How to Price Discriminate When Tariff Size Matters ”) is an assistant professor with a joint appointment in the departments of mathematics and economics at the University of Kentucky. His research interests include price discrimination using nonlinear pricing mechanisms, game theory, and mathematical economics. Hemant K. Bhargava (“ How to Price Discriminate When Tariff Size Matters ”) is an associate dean and the Jerome and Elsie Suran Professor of Technology Management at the Graduate School of Management, University of California, Davis. He studies business strategy and competition for technology products such as information goods, online services, software, electronic gadgets, media and entertainment goods, and alternative energy technologies. Yuxin Chen (“ The Benefit of Uniform Price for Branded Variants ”) is the Polk Brothers Professor in Retailing and professor of marketing at the Kellogg School of Management, Northwestern University. Currently, he is visiting the China Europe International Business School as the Zhongkun Group Visiting Chair Professor of Marketing. His primary research areas include competitive strategies, database marketing, structural empirical models, Bayesian econometric methods, and behavioral economics. His research has appeared in journals such as Marketing Science, the Journal of Marketing Research, Management Science, and Quantitative Marketing and Economics. Pradeep Chintagunta (“ Editorial—Marketing Science: A Strategic Review ”) is the Joseph T. and Bernice S. Lewis Distinguished Service Professor of Marketing at the Booth School of Business, University of Chicago. He graduated from Northwestern University and has also served on the faculty of the Johnson School, Cornell University. He is interested in studying consumer, agent, and firm behavior. In particular, he is interested in measuring the effectiveness of marketing activities in pharmaceutical markets, investigating aspects of technology product markets, studying online and off-line purchase behavior, and analyzing household purchase behavior using scanner data. Tony Haitao Cui (“ The Benefit of Uniform Price for Branded Variants ”) is an assistant professor of marketing at the Carlson School of Management, University of Minnesota, where he teaches Ph.D., EMBA, MBA, and undergraduate courses. He received a B.Eng. in fluid machinery and fluid engineering, a B.Eng. in industrial engineering, and an IMBA, all from Tsinghua University; he holds an M.S. in operations and information management and a Ph.D. in managerial science and applied economics, both from the Wharton School. His research focuses on behavioral modeling in marketing, behavioral and experimental economics, competitive strategies, distribution channels, pricing, and marketing-operations interfaces. His research has appeared in journals such as Marketing Science, Management Science, and Marketing Letters. He was named the 2011 Marketing Science Institute Young Scholar. Yiting Deng (“ Invited Paper—A Keyword History of Marketing Science ”) is a Ph.D. candidate in marketing at the Fuqua School of Business, Duke University. She received her B.A. in economics, B.S. in statistics, and M.A. in economics from Peking University before joining the Ph.D. program. She also holds a M.S. in statistics from Duke University. Her research interests include social media, advertising, online search, and choices. Dennis Fok (“ Moderating Factors of Immediate, Gross, and Net Cross-Brand Effects of Price Promotions ”) is a professor of applied econometrics at the Econometric Institute, Erasmus University Rotterdam. His research interests are in the fields of marketing and applied econometrics. These interests include modeling choice at an individual level as well as at an aggregated level; furthermore, he is interested in nonlinear panels and simulation-based estimation. He publishes on these topics in journals as Marketing Science, the Journal of Marketing Research, the Journal of Applied Econometrics, and the Journal of Econometrics. Brett R. Gordon (“ Advertising Effects in Presidential Elections ”) is the Class of 1967 Associate Professor of Business at Columbia Business School. He received his Ph.D. in economics from Carnegie Mellon University in 2007 and joined Columbia Business School the same year. His research focuses on empirical industrial organization, with an emphasis on questions pertaining to pricing, innovation, advertising, dynamic oligopoly, and competitive strategy. Rajdeep Grewal (“ Stock Market Reactions to Customer and Competitor Orientations: The Case of Initial Public Offerings ”) is the Irving & Irene Bard Professor of Marketing at the Smeal College of Business at the Pennsylvania State University; he is also the associate research director of the Institute for the Study of Business Markets there. He received his Ph.D. in 1998 from the University of Cincinnati. His research focuses on empirically modeling strategic marketing issues and has appeared in prestigious journals such as the Journal of Marketing, Journal of Marketing Research, Marketing Science, Management Science, Quantitative Marketing and Economics, and Strategic Management Journal. Currently, he serves as an associate editor for the Journal of Marketing Research and an area editor for the Journal of Marketing. Dominique Hanssens (“ Editorial—Marketing Science: A Strategic Review ”) is the Bud Knapp Distinguished Professor of Marketing at the UCLA Anderson School of Management, where he has been on the faculty since 1977. His research focuses on quantitative models that improve our understanding of marketing impact on business performance. From 2005 to 2007, he served as Executive Director of the Marketing Science Institute in Cambridge, MA. In 2010, he was elected a fellow of the INFORMS Society for Marketing Science. Wesley R. Hartmann (“ Advertising Effects in Presidential Elections ”) is an associate professor of marketing at the Stanford Graduate School of Business. He holds a Ph.D. in economics from the University of California, Los Angeles. He is interested in applying and developing econometric techniques to analyze questions relevant to marketing and economics. His current research focuses on dynamic choice contexts, pricing, social interactions, and targeted marketing. John R. Hauser (“ Editorial—Marketing Science: A Strategic Review ”) is the Kirin Professor of Marketing at the MIT Sloan School of Management, where he teaches new product development, marketing management, competitive marketing strategy, and research methodology. He has consulted for a variety of corporations on product development, sales forecasting, marketing research, voice of the customer, defensive strategy, and research and development management. Among his awards include the Converse Award for contributions to the science of marketing and the Parlin Award for contributions to marketing research. He is a founder and principal at Applied Marketing Science, Inc., a former trustee of the Marketing Science Institute, a fellow of INFORMS and of the INFORMS Society of Marketing Science, and serves on many editorial boards. He enjoys sailing, NASCAR, opera, and country music. Csilla Horváth (“ Moderating Factors of Immediate, Gross, and Net Cross-Brand Effects of Price Promotions ”) is an assistant professor of marketing at the Institute for Management Research, Radboud University, Nijmegen, the Netherlands. Her research interests include modeling dynamic marketing processes, branding, self-control, and harmful consumer behavior. She publishes in journals such as the Journal of Marketing Research, International Journal of Research in Marketing, Marketing Letters, and International Journal of Forecasting. Dmitri Kuksov (“ A Model of the “It' Products in Fashion ”) is a professor of marketing at the Naveen Jindal School of Management, the University of Texas at Dallas; previously, he worked at Washington University in St. Louis. He holds a Ph.D. in marketing from Haas Business School of the University of California, Berkeley. His research interests include competitive strategy, markets with incomplete information, consumer communication and networks, branding and product line strategy, and customer satisfaction. He received 2005 Frank M. Bass Dissertation Award; two of his papers were finalists for 2007 John D. C. Little Award, and one was a finalist for INFORMS 2012 Long Term Impact Award. He is an associate editor of Marketing Science, Management Science, and Quantitative Marketing and Economics. Vardit Landsman (“ The Relationship Between DTCA, Drug Requests, and Prescriptions: Uncovering Variation in Specialty and Space ”) is an assistant professor of marketing at the Recanati Business School, Tel Aviv University (Israel), and the Erasmus School of Economics, Erasmus University Rotterdam (the Netherlands). Her fields of interest include the implementation of new modeling approaches to the study of marketing phenomena. Her work involves the study consumer choice and, in particular, the analysis of choice processes within new markets, as well as the study of marketing issues in the context of life sciences. Her work has been published in the Journal of Marketing and Quantitative Marketing and Economics. Carl F. Mela (“ Invited Paper—A Keyword History of Marketing Science ”) is the T. Austin Finch Foundation Professor of Marketing at Duke University, where he teaches brand management and the marketing core. His research focuses on the long-term effects of marketing activity and new media. His articles appear in the Journal of Marketing Research, Marketing Science, the Journal of Marketing, the Harvard Business Review, and t

Greg M. Allenby (“ The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis ”) is the Helen C. Kurtz Chair of Marketing at the Max M. Fisher College of Business, the Ohio State University. Eva Ascarza (“ A Joint Model of Usage and Churn in Contractual Settings ”) is an assistant professor of marketing at the Columbia Business School. She is a marketing modeler who uses tools from statistics and economics to answer marketing questions. Her main research areas are customer analytics and pricing in the context of subscription businesses. Ron Berman (“ The Role of Search Engine Optimization in Search Marketing ”) is a doctoral candidate at the Haas School of Business, University of California, Berkeley. He holds an M.Sc. in computer science from Tel Aviv University and a B.Sc. in physics, math and computer science from the Hebrew University in Jerusalem. His research focuses on new media, online advertising, start-ups, and online phenomena in general. Eyal Biyalogorsky (“ Complementary Goods: Creating, Capturing, and Competing for Value ”) is an associate professor of marketing at the Arison School of Business, the Interdisciplinary Center (IDC) Herzliya, Israel. He received his Ph.D. in marketing from the Fuqua School of Business, Duke University, and was on the faculty of the University of California, Davis, before joining the Arison School of Business. He wants to point out that the work with his complementary coauthors on this paper was an exception to the quality issues discussed in the paper. Joachim Büschken (“ The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis ”) is a professor of marketing at the Ingolstadt School of Management, Catholic University of Eichstätt-Ingolstadt, Germany. Stefano Colombo (“ Product Differentiation and Collusion Sustainability When Collusion Is Costly ”) is an assistant professor of economics at the Università Cattolica del Sacro Cuore, Milan. He has a magna cum laude degree in economics from Bocconi University and holds a Ph.D. in economics (DEFAP) from the Università Cattolica del Sacro Cuore. His research interests are industrial economics, regional economics and spatial methods. He has published in academic journals such as Papers in Regional Science, Games and Economic Behavior, and Annals of Regional Science. Pedro M. Gardete (“ Cheap-Talk Advertising and Misrepresentation in Vertically Differentiated Markets ”) is an assistant professor of marketing at the Stanford Graduate School of Business. His research focuses on marketing strategies related to advertising and on the role of market information in strategic contexts. Zheyin (Jane) Gu (“ Consumer Fit Search, Retailer Shelf Layout, and Channel Interaction ”) is an assistant professor of marketing at the School of Business, University at Albany, State University of New York. She received Ph.D. in marketing from the Stern School of Business, New York University. Her research interests include distribution channel, retailing, e-commerce, and competitive strategies. She has published in the Journal of Marketing Research, Management Science, and Marketing Science. Mehmet Gümüş (“ Returns Policies Between Channel Partners for Durable Products ”) is an assistant professor in the Operations Management Area at the Desautels Faculty of Management, McGill University. He joined McGill in 2007 from the University of California, Berkeley, where he completed his Ph.D. in industrial engineering and operations research and his M.A. in economics. In his research, he explores the impact of customer behavior and information asymmetry on supply chain management, dynamic pricing, and risk management. His research has been published in Management Science, Operations Research, Manufacturing & Service Operations Management, Marketing Science, and Production and Operations Management. Bruce G. S. Hardie (“ A Joint Model of Usage and Churn in Contractual Settings ”) 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. Zsolt Katona (“ The Role of Search Engine Optimization in Search Marketing ”) is an assistant professor of marketing at the Haas School of Business, University of California, Berkeley. He has a Ph.D. in management from INSEAD; he previously earned a Ph.D. in computer science from Eotvos University, Budapest. His current research focuses on understanding the interaction between websites' online advertising strategies, and he also studies the role that link structure of social networks plays in word-of-mouth effects and community formation. His research on online marketing has been published in Marketing Science, Management Science, and the Journal of Marketing Research. Previously, he had analyzed characteristics of different random networks and published his work in journals such as the Journal of Applied Probability, Statistics and Probability Letters, and Random Structures and Algorithms. Oded Koenigsberg (“ Complementary Goods: Creating, Capturing, and Competing for Value ”) is an associate professor of marketing at the London Business School. His research interest is the marketing–manufacturing interface—in particular, in incorporating operational constraints into firms' marketing decisions (e.g., pricing, channel, product design and product line). His research has appeared in Quantitative Marketing and Economics, Management Science, Production and Operations Management, the Journal of Marketing Research, and Marketing Science. He is an associate editor at the International Journal of Research in Marketing and serves on the editorial boards of Marketing Science and Production and Operations Management. Yunchuan Liu (“ Consumer Fit Search, Retailer Shelf Layout, and Channel Interaction ”) is an associate professor of business administration at the College of Business, University of Illinois at Urbana–Champaign. He received Ph.D. in marketing from Columbia University. His research interests include distribution channels, retailing, product strategy, and pricing strategy. Many of his papers have been published in Marketing Science and Management Science. Chakravarthi Narasimhan (“ National Brand's Response to Store Brands: Throw In the Towel or Fight Back? ”) is the Philip L. Siteman Professor of Marketing at the Olin Business School, Washington University in St. Louis. His current research interests are in strategic value of information, incorporating non-microeconomic foundations in strategic models, understanding the impact of promotions on brands, examining the interaction of multiple marketing strategies, and supply chain contracts, especially supply chain strategies under uncertainty. He has published in Marketing Science, Management Science, the Journal of Marketing Research, the Journal of Marketing, the Journal of Business, the Journal of Econometrics, and Harvard Business Review, among others. He is an area editor of Marketing Science and is an associate editor of Quantitative Marketing and Economics. Sherif Nasser (“ National Brand's Response to Store Brands: Throw In the Towel or Fight Back? ”) is an assistant professor of marketing at the Olin Business School, Washington University in St. Louis. He holds a Ph.D. in marketing from New York University's Stern School of Business. His research interests are in product differentiation, media and advertising, distribution channels, and the interface of marketing and operations management. Elie Ofek (“ Complementary Goods: Creating, Capturing, and Competing for Value ”) is the T.J. Dermot Dunphy Professor of Business Administration at the Harvard Business School. He received his Ph.D. in business and M.A. in economics from Stanford University. His research focuses on the relationship between marketing and innovation strategy and on how firms can leverage novel technologies or major trends to deliver value to customers. His research has appeared in Marketing Science, Management Science, the Journal of Marketing Research, the Journal of Consumer Research, and the Journal of Economics and Management Strategy. He is an associate editor at Management Science and serves on the editorial boards of Marketing Science, the Journal of Marketing Research, and the International Journal of Research in Marketing. Thomas Otter (“ The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis ”) is a professor of marketing at the Faculty of Business and Economics, Johann Wolfgang Goethe University, Frankfurt am Main, Germany. Saibal Ray (“ Returns Policies Between Channel Partners for Durable Products ”) is an associate professor in the Operations Management Area at the Desautels Faculty of Management, McGill University. His research interest can broadly be categorized as value chain management; he is specifically interested in studying value chain risk management, contracting/competition issues in value chains, time-based competition, issues related to used goods markets, capacity and inventory management, and dynamic pricing. His research has been published in such reputed journals as Management Science, Operations Research, Manufacturing & Service Operations Management, Marketing Science, and Production and Operations Management. In addition, he has held (or presently holds) a number of grants from the governments of Canada and Quebec. He has been awarded th

The information processing literature provides a wealth of laboratory evidence on the effects that the choice task and individual characteristics have on the extent to which consumers engage in alternative-based versus attribute-based information processing. Less attention has been paid to studying how the processing pattern at the point of purchase is associated with a consumer's propensity to buy in shopping settings. To understand this relationship, we formulate a discrete choice model and perform formal model comparisons to distinguish among several possible dependence structures. We consider models involving an existing measure of information processing, PATTERN; a latent variable version of this measure; and several new refinements and generalizations. Analysis of a unique data set of 895 shoppers on a popular electronics website supports the latent variable specification and provides validation for several hypotheses and modeling components. We find a positive relationship between alternative-based processing and purchase, as well as a tendency of shoppers in the lower price category to engage in alternative-based processing. The results also support the case for joint modeling and estimation. These findings can be useful for future work in information processing and suggest that likely buyers can be identified while engaged in information processing prior to purchase commitment, an important first step in targeting decisions.

This paper considers the history of keywords used in Marketing Science to develop insights on the evolution of marketing science. Several findings emerge. First, “pricing” and “game theory” are the most ubiquitous words. More generally, the three C's and four P's predominate, suggesting that keywords and common practical frameworks align. Various trends exist. Some words, like “pricing,” remain popular over time. Others, like “game theory” and “hierarchical Bayes,” have become more popular. Finally, some words are superseded by others, like “diffusion” by “social networking.” Second, the overall rate of new keyword introductions has increased, but the likelihood they will remain in use has decreased. This suggests a maturation of the discipline or a long-tail effect. Third, a correspondence analysis indicates three distinct eras of marketing modeling, comporting roughly with each of the past three decades. These eras are driven by the emergence of new data and business problems, suggesting a fluid field responsive to practical problems. Fourth, we consider author publication survival rates, which increase up to six papers and then decline, possibly as a result of changes in ability or motivation. Fifth, survival rates vary with the recency and nature of words. We conclude by discussing the implications for additional journal space and the utility of standardized classification codes.

I measure the spillover effect of intercollegiate athletics on the quantity and quality of applicants to institutions of higher education in the United States—an effect popularly known as the “Flutie effect.” I treat athletic success as a stock of goodwill that decays over time, similar to that of advertising. A major challenge is that privacy laws prevent us from observing information about the applicant pool. I overcome this challenge by using order statistic distribution to infer applicant quality from information on enrolled students. Using a flexible random-coefficients aggregate discrete choice model that accommodates heterogeneity in preferences for school quality and athletic success, as well as an extensive set of school fixed effects to control for unobserved quality in athletics and academics, I estimate the impact of athletic success on applicant quality and quantity. Overall, athletic success has a significant, long-term goodwill effect on future applications and quality. However, students with lower-than-average SAT scores tend to have a stronger preference for athletic success, whereas students with higher SAT scores have a greater preference for academic quality. Furthermore, the decay rate of athletics' goodwill is significant only for students with lower SAT scores, suggesting that the goodwill created by intercollegiate athletics resides more extensively with lower-scoring students than with their higher-scoring counterparts. But, surprisingly, athletic success impacts applications even among academically stronger students.

Retailers face the problem of finding the assortment that maximizes category profit. This is a challenging task because the number of potential assortments is very large when there are many stock-keeping units (SKUs) to choose from. Moreover, SKU sales can be cannibalized by other SKUs in the assortment, and the more similar SKUs are, the more this happens. This paper develops an implementable and scalable assortment optimization method that allows for theory-based substitution patterns and optimizes real-life, large-scale assortments at the store level. We achieve this by adopting an attribute-based approach to capture preferences, substitution patterns, and cross-marketing mix effects. To solve the optimization problem, we propose new very large neighborhood search heuristics. We apply our methodology to store-level scanner data on liquid laundry detergent. The optimal assortments are expected to enhance retailer profit considerably (37.3%), and this profit increases even more (to 43.7%) when SKU prices are optimized simultaneously.

There is a growing trend among consumers to serially consume small, incomplete “chunks” of multiple media types—television, radio, Internet, and print—within a short time period. We refer to this behavior as media multiplexing and note that key challenges for integrated marketing communications media planners are (1) predicting which media or combination of media their target audience is likely to consume at any given time and (2) understanding potential substitutions and complementarities in their joint consumption. We propose a forecasting model that incorporates media-multiplexing behavior of both traditional and new media, their interdependencies, and consumer heterogeneity, and we calibrate the model using a rich database of individual-specific media activity diaries. The results suggest that accounting for media synergies within a single utility specification significantly improves model forecasts. We also introduce a utility function that directly models cross-channel media complementarities via interactive effects of the satiation parameters of own and joint consumption of various media types. Finally, our individual-level analyses generate unique insights on consumer-level media switching, multiplexing, and individual heterogeneity often ignored in aggregate data.

Researchers and practitioners devote substantial effort to targeting banner advertisements to consumers, but they focus less effort on how to communicate with consumers once targeted. Morphing enables a website to learn, automatically and near optimally, which banner advertisements to serve to consumers to maximize click-through rates, brand consideration, and purchase likelihood. Banners are matched to consumers based on posterior probabilities of latent segment membership, which are identified from consumers' clickstreams. This paper describes the first large-sample random-assignment field test of banner morphing—more than 100,000 consumers viewed more than 450,000 banners on CNET.com. On relevant Web pages, CNET's click-through rates almost doubled relative to control banners. We supplement the CNET field test with an experiment on an automotive information-and-recommendation website. The automotive experiment replaces automated learning with a longitudinal design that implements morph-to-segment matching. Banners matched to cognitive styles, as well as the stage of the consumer's buying process and body-type preference, significantly increase click-through rates, brand consideration, and purchase likelihood relative to a control. The CNET field test and automotive experiment demonstrate that matching banners to cognitive-style segments is feasible and provides significant benefits above and beyond traditional targeting. Improved banner effectiveness has strategic implications for allocations of budgets among media.

Several key questions in bundling have not been empirically examined in marketing: Is mixed bundling more effective than pure bundling or pure components? Does correlation in consumer valuations make bundling more or less effective? Does bundling serve as a complement or substitute to network effects? To address these questions, we develop a consumer-choice model from microfoundations to capture the essentials of our setting, the handheld video game market. We provide a framework to understand the dynamic, long-term effects of bundling on demand. The primary explanation for the profitability of bundling relies on homogenization of consumer valuations for the bundle, allowing the firm to extract more surplus. We find that bundling can be effective through a novel and previously unexamined mechanism of dynamic consumer segmentation, which operates independent of the homogenization effect, and can in fact be stronger when the homogenization effect is weaker. We also find that bundles are treated as separate products (distinct from component products) by consumers. Sales of both hardware and software components decrease in the absence of bundling, and consumers who had previously purchased bundles might delay purchases, resulting in lower revenues. We also find that mixed bundling dominates pure bundling and pure components in terms of both hardware and software revenues. Investigating the link between bundling and indirect network effects, we find that they act as substitute strategies, with a lower relative effectiveness for bundling when network effects are stronger.