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We document the “good-on-paper effect,” whereby people make more virtuous decisions on paper than on a digital device (e.g., tablet).

This paper introduces a new decision support tool to optimize advertising campaigns for new product launches based on learnings from an ex post analysis of prior campaigns.

This paper addresses the repositioning of Kmart Australia in 2011. It shows how by calibrating emotional as well as cognitive reactions and estimating their impact on purchase intentions, Kmart was able to focus its communications, improving market share. We measure nine key emotions, ranging from surprise to anger. Including these emotions significantly improves our model for likelihood to choose a store. Measuring emotions enabled Kmart’s advertising agency to create a television commercial that tapped into the specific emotions that most strongly predict the likelihood to choose a store; that is, the model drove the development of the advertising creative. The resulting television commercial tested well and was effective when launched. At the individual level, cognitions and emotions changed dramatically. At the aggregate level, an econometric model showed that store visits were significantly enhanced. Kmart’s EBIT (earnings before interest and tax) increased by 30%, whereas Kmart’s main rival had almost no EBIT growth, despite vigorous attempts to counter Kmart’s campaign. One of our key contributions is to incorporate emotions into marketing science models of evaluation and purchase intentions. We also provide a new methodology to measure emotions. The approach enables marketing science to participate in the design of marketing stimuli, rather than just testing preexisting ones. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0954 .

Georgia Aquarium (GA), a non-profit organization, is the world’s second largest aquarium (by water volume) and is among the most popular tourist destinations in the United States. Recently GA management has observed that the organization’s growth trajectory is stagnating. While other aquariums face a similar trend, GA wants to be proactive and reverse this direction. They face a multifaceted business challenge involving four conflicting objectives: (1) How to increase revenues without increasing ticket prices; (2) how to increase attendance without compromising visitor satisfaction; (3) how to increase the impact of media investments without spending more; (4) how to attract customers with long-term value potential. To address these challenges, we developed an integrated approach consisting of multiple marketing science models including Data Envelopment Analysis, Competition Analysis, Spatial Analysis, Media Optimization Analysis, and Pass Holder Lifetime Net Revenue Analysis. Based on the findings of our analyses and the parameter estimates of our models, GA proceeded with a field implementation to validate our suggestions. As a result, they realized a 10% increase in attendance and a 12% increase in revenue in 2013, thereby enhancing their bottom line and growth. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0932 .

The purpose of this paper is to describe the implementation of a category management tool known as Category Optimizer™ at Foster's Wine Estates Americas for one of its brands, the Beringer California Collection. Foster's was facing a common management problem: harnessing its portfolio of Beringer California Collection wines to increase profitability, improve its competitive position, and defend against a disruptive new entrant in the U.S. wine market called Yellow Tail. Category Optimizer combines the parsimony of an internal market structure with the advances that have been made in assortment planning in operations research, assortment and stock-keeping-unit–level modeling, mixed logits, and the marketing literature on the perceptions of variety of assortment to develop and estimate a model on readily available store scanner data. The model subsequently uses these results to inform strategic and tactical decision making. This approach led to recommendations that initially seemed counterintuitive; the normal response would be for Foster's to consider lowering prices to maintain share and volume, a strategy not inconsistent with many of the recommendations of past models. However, considering the additional degrees of freedom that a product range offered for defense, we demonstrated that a combination of price increases together with the introduction of a volume-flanker product in a new channel would improve profits, increase revenue, and protect and enhance market share. These were successfully implemented in early 2008, earning rich dividends for the company; increasing profitability by 70%, revenue by 3%, and earnings before interest and taxes by 8.5%; and having a positive impact on its brand ranking. In fact, in 2008, it debuted as sixth among the international wine brands. It also managed to play an important role in deposing Yellow Tail, the market share leader, from its dominant position. We conclude the paper by providing examples of other companies where this approach has also been successfully implemented and by discussing some avenues for future research.

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.

Marketing is a field that is rich in data. Our data is of high quality, often at a highly disaggregate level, and there is considerable variation in the key variables for which estimates of effects on outcomes such as sales and profits are desired. The recognition that, in some general sense, marketing variables are set by firms on the basis of information not always observable by the researcher has led to concerns regarding endogeneity and widespread pressure to implement instrumental variables methods in marketing problems. The instruments used in our empirical literature are rarely valid and the IV methods used can have poor sampling properties, including substantial finite sample bias and large sampling errors. Given the problems with IV methods, a convincing argument must be made that there is a first order endogeneity problem and that we have strong and valid instruments before these methods should be used. If strong and valid instruments are not available, then researchers need to look toward supplementing the information available to them. For example, if there are concerns about unobservable advertising or promotional variables, then the researcher is much better off measuring these variables rather than using instruments (such as lagged marketing variables) that are clearly invalid. Ultimately, only randomized variation in marketing variables (with proper implementation and large samples) can be argued to be a valid instrument without further assumptions.

Learning models extend the traditional discrete choice framework by postulating that consumers have incomplete information about product attributes and that they learn about these attributes over time. In this survey we describe the literature on learning models that has developed over the past 20 years, using the model of Erdem and Keane as a unifying framework [Erdem T, Keane M (1996) Decision-making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Sci. 15(1):1–20]. We describe how subsequent work has extended their modeling framework and applied learning models to a wide range of different products and markets. We argue that learning models have contributed greatly to our understanding of consumer behavior—in particular, in enhancing our understanding of brand loyalty and long-run advertising effects. We also discuss the limitations of existing learning models and potential extensions. One key challenge is to disentangle learning as a source of dynamics from other key mechanisms that may generate choice dynamics (inventories, habit persistence, etc.). Another is to enhance identification of learning models by collecting and using direct measures of signals, perceptions, and expectations.

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 IRI's fees (for those who need it) are available through the INFORMS Society for Marketing Science (ISMS). Please see the website above for further details.

Marketing researchers have used models of consumer demand to forecast future sales, to describe and test theories of behavior, and to measure the response to marketing interventions. The basic framework typically starts from microfoundations of expected utility theory to obtain an econometric system that describes consumers' choices over available options, and to thus characterize product demand. The basic framework has been augmented significantly to account for quantity choices, to accommodate purchases of several products on a single purchase occasion (multiple discreteness and multicategory purchases), and to allow for asymmetric switching between brands across different price tiers. These extensions have enabled researchers to bring the analysis to bear on several related marketing phenomena of interest. This paper has three main objectives. The first objective is to articulate the main goals of demand analysis—forecasting, measurement, and testing—and to highlight several considerations associated with these goals. Our second objective is to describe the main building blocks of individual-level demand models. We discuss approaches built on direct and indirect utility specifications of demand systems, and we review extensions that have appeared in the marketing literature. The third objective is to explore a few emerging directions in demand analysis, including considering demand-side dynamics, combining purchase data with primary information, and using semiparametric and nonparametric approaches. We hope researchers new to this literature will take away a broader perspective on these models and see the potential for new directions in future research.

This paper provides a critical review of the methods for estimating static discrete games and their relevance for quantitative marketing. We discuss the various modeling approaches, alternative assumptions, and relevant trade-offs involved in taking these empirical methods to data. We consider games of both complete and incomplete information, examine the primary methods for dealing with the coherency problems introduced by multiplicity of equilibria, and provide concrete examples from the literature. We illustrate the mechanics of estimation using a real-world example and provide the computer code and data set with which to replicate our results.

In addition to retailing new vehicles, automotive manufacturers in the United States sell millions of vehicles through leasing and to fleet customers every year. The majority of these vehicles are returned to the automotive manufacturers at the end of the contracted term and must be “remarketed.” In 2007, about 10 million used vehicles were sold at more than 400 auctions in the United States. Large consigners face decisions every day about when, where, and at what price to offer these vehicles, which has significant financial implications for their profitability. To address the challenges of the distribution process, Power Information Network (PIN), a division of J.D. Power and Associates, developed the PIN Optimal Distribution of Auction Vehicles System (ODAV), an automated decision optimization system that helps remarketers maximize profits through the most advantageous distribution of their auction vehicles. At the core of the system is a combination of three models that determine the distribution of the vehicles on a daily basis: a nearest neighbor linear regression model for short-term auction price forecasting; an autoregressive integrated moving average time-series analysis model for volume-price elasticity; and a genetic algorithm optimizer for vehicle distribution. Since its launch in 2003, PIN has been providing ODAV services on a daily basis, and to date, more than two million vehicles have been distributed through this system. In this paper, we will describe the PIN ODAV System, its implementation, and the business impact by using as an example the experience with our largest client, Chrysler Group LLC.

In this note I overview the data selection and procurement process in the context of structural models. Data selection for structural models presents unique challenges because data and structure often substitute and because it is imperative to consider what information identifies causal effects of interest. I further discuss three types of field data on which to build empirical models: (i) data that are proprietary to firms, (ii) data that can come from the public domain, or (iii) data that can be purchased from private research firms, and I discuss the benefits and limits of each. I then detail a process for obtaining proprietary data and the potential pitfalls inherent in the process.

A variable annuity is a popular product for investing retirement income. However, thousands of similar-looking variable annuity products are being offered by hundreds of financial service companies. In such a scenario, how can Prudential achieve meaningful product differentiation to increase the sales of its variable annuities? The solution led to the development and implementation of the “Emotion Quotient” (EQ) Tool. The EQ Tool enabled Prudential to redefine its marketing and sales approach along a proactive (as opposed to responsive) market orientation paradigm. This was accomplished by first using the EQ Tool to uncover and quantify the prevalence of certain emotions (such as fear and regret) in the prospective consumer and then pitching relevant variable annuity product(s) that could mitigate the specific behavioral risk corresponding to the prevalent emotion(s). This approach, which was backed by extensive research (as described in this study), enabled Prudential to gain over $450 million lift in variable annuity sales and contributed to consumer welfare by promoting awareness of behavioral risk to investors who are within five years of their retirement. This research study illustrates how industry can collaborate with academia to successfully apply marketing science to solve real-world business problems.

Procter & Gamble (P&G) Asia-Pacific is interested in managing value growth. Only after fully understanding the true effects of the marketing-mix variables can P&G managers make strategic decisions answering questions such as the following: (1) Are the P&G brands in the detergent market inelastic or elastic with respect to price? How has the price elasticity changed over time? Can P&G increase the price of its brands to gain value growth? (2) What are the price, distribution, and sizing combinations needed to achieve the desirable value growth? (3) How can P&G gain market share from its competitors without cannibalizing its own brands? P&G Asia-Pacific approached us to develop a value growth framework to answer these questions. To generate the answers for the above questions, we develop a three-step weighted random coefficient estimator that captures the heterogeneity across cross sections (different stock-keeping units and states) and the endogeneity of distribution. Based on the parameter estimates, we provide strategic recommendations to P&G for a field test to validate our suggestions. We developed a simulator for P&G managers so that they can generate appropriate marketing-mix strategies for achieving the desired value growth. As a result, P&G gained over $39 million in value growth over a one-year period by implementing the recommendations from our modeling approach.

We develop a robust model for estimating, tracking, and managing brand equity for multicategory brands based on customer survey and financial measures. This model has two components: (1) offering value (computed from discounted cash flow analysis) and (2) relative brand importance (computed from brand choice models such as multinomial logit, heteroscedastic extreme value, and mixed logit). We apply this model to estimate the brand equity of Allstate—a leading insurance company—and its leading competitor, which compete in multiple categories. The model captures the brand's spillover effects from one category to another. In addition, we identify the dimensions that drive a brand's image, examine the relationships among advertising, brand equity, and shareholder value, and build a decision support simulator for the focal brand. Our model provides reliable estimates of brand equity, and our results show that advertising has a strong long-term positive influence on brand equity, which is significantly positively related to shareholder value. The model, the brand equity estimates, and the decision support simulator are used by key executives across multiple functional areas and have enabled the company to substantially gain by reallocating its advertising resources to improve brand equity and shareholder value, and by offering better guidance to analysts and investors.

No abstract available.

A call for funded proposals that was jointly sponsored by the Wharton Interactive Media Initiative (http://www.whartoninteractive.com) and the Marketing Science Institute (http:// www.msi.org) on UGC demonstrated a strong interest in a variety of research approaches (e.g., experimental, analytical theory, empirical structural, statistical, and data mining) that are being used to study the creation of and impact of UGC. Marketing Science strongly supports this area as important to our field—hence, this call for papers for a special issue. Marketing Science is looking for innovative and groundbreaking papers that address issues related to UGC. Examples of paper topics might include the following