Marketing Science

doi: 10.1287/mksc.1080.0478pmid: N/A

We introduce the work of the finalists in the 2007 ISMS Practice Prize Competition, representing the best examples of rigor plus relevance that our profession produces. The winner, describing a collaboration between National Academies Press and a team based at the University of Maryland, involves a sequenced program of research to calibrate price sensitivity of digital media. The other two finalists address a model to optimize the distribution of used cars geographically depending on local conditions, and a model to use price and distribution strategies to improve product profitability of P&G's heavy-duty detergent brands in India.

Kannan, P. K.; Pope, Barbara Kline; Jain, Sanjay

doi: 10.1287/mksc.1080.0481pmid: N/A

We examine the problem of how a content provider, specifically the National Academies Press (NAP), can optimally price the different forms of its productprint and PDFthat it sells online. Whereas products in the traditional product line generally tend to be substitutes, the different content product forms could range from being substitutes to being complements across customers. Thus the content provider can possibly sell bundles of the product forms, leading to additional revenue. We first discuss NAP's decision context and describe the model we proposed for developing NAP's optimal pricing policies for its different forms. We describe the choice experiment we conducted on the publisher's website that maximally uses the online interface to collect relevant data needed to estimate our model. We show how NAP embraced the results from the model for developing a new business model and how it used the insights derived from the study to set pricing policies and monitor sales performance as a function of pricing. Finally, we perform validation of the model and the implemented policies using dynamic modeling of sales data from NAP's website. The paper illustrates how e-commerce technologies can lead to the development of optimal policies using marketing models.

Du, Jie; Xie, Lili; Schroeder, Stephan

doi: 10.1287/mksc.1080.0470pmid: N/A

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.

Kumar, V.; Fan, Jia; Gulati, Rohit; Venkat, P.

doi: 10.1287/mksc.1080.0477pmid: N/A

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.

Hansen, Karsten; Singh, Vishal

doi: 10.1287/mksc.1080.0432pmid: N/A

We study how market structure within a product category varies across retail formats. Building on the literature on internal market structure, we estimate a joint store and brand choice model where the loading matrix of brand attributes are allowed to be retail format specific. The approach allows us to recover brand maps for different retail formats while controlling for the short-term marketing mix activities at these stores and the self-selection of households that frequent a particular format. The model is applied to consumer panel data from two product categories, where households are observed to make purchases across three store types: high-end grocery store, traditional supermarket, and large everyday low pricing (EDLP) formats. Our results show strong correlations between the marketing mix sensitivities, store format preference, and unobserved brand attributes. These correlations translate into significant differences in market structure across retail formats and in the direction and size of preference vectors for unobservable brand attributes. We find a tight clustering of brands at the EDLP format, whereas brands are found to compete in distinct subgroups at other stores. Results show that failure to account for retail format effects can substantially bias the understanding of underlying market structure and could lead to incorrect implications in applications such as new product entry.

de Jong, Martijn G.; Steenkamp, Jan-Benedict E. M.; Veldkamp, Bernard P.

doi: 10.1287/mksc.1080.0439pmid: N/A

In the last few decades, the measurement of marketing constructs has improved tremendously. Our discipline has also started to systematically catalogue our measurement knowledge in influential handbooks of marketing scales. However, at least two important issues remain. First, existing scales are often too long for administration in nonstudent samples or in applied studies. Second, existing (U.S.-developed) scales may contain items that are not informative about the underlying construct in particular countries, whereas relevant items tapping into local cultural expressions of the construct in question may be missing. To address these issues, we propose a new model that yields country-specific yet fully cross-nationally comparable short forms of unidimensional marketing scales. The procedure is based on hierarchical item response theory and optimal test design. The procedure is flexible in the sense that the researcher can specify various constraints on item content, scale length, and measurement precision. Because our procedure allows inclusion of country-specific (or emic) items in standardized (or etic) scales, it presents an important step toward resolving the emic-etic dilemma that has plagued international marketing research for decades.

Stremersch, Stefan; Lemmens, Aurlie

doi: 10.1287/mksc.1080.0440pmid: N/A

Prior marketing literature has overlooked the role of regulatory regimes in explaining international sales growth of new products. This paper addresses this gap in the context of new pharmaceuticals (15 new molecules in 34 countries) and sheds light on the effects of regulatory regimes on new drug sales across the globe. Based on a time-varying coefficient model, we find that differences in regulation substantially contribute to cross-country variation in sales. One of the regulatory constraints investigated, i.e., manufacturer price controls, has a positive effect on drug sales. The other forms of regulation such as restrictions of physician prescription budgets and the prohibition of direct-to-consumer advertising (DTCA) tend to hurt sales. The effect of manufacturer price controls is similar for newly launched and mature drugs. By contrast, regulations on physician prescription budgets and DTCA have a differential effect for newly launched and mature drugs. Whereas the former hurts mature drugs more, the latter has a larger effect on newly launched drugs. In addition to these regulatory effects, we find that national culture, economic wealth, and lagged sales also affect drug sales. Our findings may be used as input by managers for international launch and marketing decisions. They may also be used by public policy administrators to assess the role of regulatory regimes in pharmaceutical sales growth.

Fox, Edward J.; Postrel, Steven; Semple, John H.

doi: 10.1287/mksc.1080.0442pmid: N/A

We propose a dynamic programming framework for retailers of frequently purchased consumer goods in which the prices affect both the profit per visit in the current period and the number of visitors (i.e., store traffic) in future periods. We show that optimal category prices in the infinite-horizon problem also maximize the closed form sum of a geometric series, allowing us to derive meaningful analytical results. Modeling the linkage between category prices and future store traffic fundamentally changes optimal pricing policy. Optimal pricing must balance current profits against future traffic; under general conditions, optimal long-run prices are uniformly lower across all categories than those that maximize current profits. This result explains the empirical generalization that category demand in grocery stores is inelastic. Parameterizing profit per visit and store traffic reveals that, as future traffic becomes more sensitive to price, retailers should increasingly lower current prices and sacrifice current profits. We also determine how the burden of drawing future traffic to the store should be distributed across categories; this is the foundation for a new taxonomy of category roles.

Godes, David; Mayzlin, Dina

doi: 10.1287/mksc.1080.0444pmid: N/A

In this paper, we investigate the effectiveness of a firm's proactive management of customer-to-customer communication. We are particularly interested in understanding how, if at all, the firm should go about effecting meaningful word-of-mouth (WOM) communications. To tackle this problem, we collect data from two sources: (1) we implement a large-scale field test in which a national firm created word of mouth through two populations: customers and noncustomers, and (2) we collect data from an online experiment. We break our theoretical problem into two subproblems. First, we ask: What kind of WOM drives sales? Motivated by previous research, we hypothesize that for a product with a low initial awareness level, WOM that is most effective at driving sales is created by less loyal (not highly loyal) customers and occurs between acquaintances (not friends). We find support for this in the field test as well as in an experimental setting. Hence, we demonstrate the potential usefulness of exogenously created WOM: conversations are created where none would naturally have occured otherwise. Then, we ask: Which agents are most effective at creating this kind of WOM? In particular, we are interested in evaluating the effectiveness of the commonly used opinion leader designation. We find that although opinion leadership is useful in identifying potentially effective spreaders of WOM among very loyal customers, it is less useful for the sample of less loyal customers.

Chen, Tao; Sun, Baohong; Singh, Vishal

doi: 10.1287/mksc.1080.0446pmid: N/A

The strategy Philip Morris adopted in 1993 featured a one-time, permanent, publicly announced price cut, an event referred to as Marlboro Friday. Little is known about the impact of permanent and publicly announced price cuts on consumer brand switching behaviors for an addictive product. In the context of Marlboro Friday, we investigate (1) how consumers' brand choices are affected by a permanent price cut, (2) whether differential and dynamic effects of permanent price cuts occur for different types of consumers, and (3) the implications of publicly announced permanent price cuts on consumer brand switching behavior in the long run. We develop a dynamic structural brand choice model that allows for consumer forward-looking behavior, learning, and addiction, and investigate how consumers adjusted their brand choice behaviors before and after this permanent price cut. Using unique consumer panel data pertaining to cigarette purchases before and after the event, we provide behavioral explanations of whether and how the drastic and permanent price cut represented an effective step to encourage brand switching for an addictive product and a necessary step for Philip Morris to combat the growing market share of generic brands.