Knowledge that Transforms

Existing research on choice designs focuses exclusively on compensatory models that assume that all available alternatives are considered in the choice process. In this paper, we develop a method to construct efficient designs for a two-stage, consider-then-choose model that involves a noncompensatory screening process at the first stage and a compensatory choice process at the second stage. The method applies to both conjunctive and disjunctive screening rules. Under certain conditions, the method also applies to the subset conjunctive and disjunctions of conjunctions screening rules. Based on the local design criterion, we conduct a comparative study of compensatory and conjunctive designs—the former are optimized for a compensatory model and the latter for a two-stage model that uses conjunctive screening in its first stage. We find that conjunctive designs have higher level overlap than compensatory designs. This occurs because level overlap helps pinpoint screening behavior. Higher overlap of conjunctive designs is also accompanied by lower orthogonality, less level balance, and more utility balance. We find that compensatory designs have a significant loss of design efficiency when the true model involves conjunctive screening at the consideration stage. These designs also have much less power than conjunctive designs in identifying a true consider-then-choose process with conjunctive screening. In contrast, when the true model is compensatory, the efficiency loss from using a conjunctive design is lower. Also, conjunctive designs have about the same power as compensatory designs in identifying a true compensatory choice process. Our findings make a strong case for the use of conjunctive designs when there is prior evidence to support respondent screening.

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.

Commercial open source software (COSS) products—privately developed software based on publicly available source code—represent a rapidly growing, multibillion-dollar market. A unique aspect of competition in the COSS market is that many open source licenses require firms to make certain enhancements public, creating an incentive for firms to free ride on the contributions of others. This practice raises a number of puzzling issues. First, why should a firm further develop a product if competitors can freely appropriate these contributions? Second, how does a market based on free riding produce high-quality products? Third, from a public policy perspective, does the mandatory sharing of enhancements raise or lower consumer surplus and industry profits? We develop a two-sided model of competition between COSS firms to address these issues. Our model consists of (1) two firms competing in a vertically differentiated market, in which product quality is a mix of public and private components, and (2) a market for developers that firms hire after observing signals of their contributions to open source. We demonstrate that free-riding behavior is supported in equilibrium, that a mandatory sharing setting can result in high-quality products, and that free riding can actually increase profits and consumer surplus.

Many firms have introduced Internet-based customer self-service applications such as online payments or brokerage services. Despite high initial sign-up rates, not all customers actually shift their dealings online. We investigate whether the multistage nature of the adoption process (an “adoption funnel”) for such technologies can explain this low take-up. We use exogenous variation in events that possibly interrupt adoption, in the form of vacations and public holidays in different German states, to identify the effect on regular usage of being interrupted earlier in the adoption process. We find that interruptions in the early stages of the adoption process reduce a customer's probability of using the technology regularly. Our results suggest significant cost-saving opportunities from eliminating interruptions in the adoption funnel.

Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper, we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities.

We discuss how regression discontinuity designs arise naturally in settings where firms target marketing activity at consumers, and we illustrate how this aspect may be exploited for econometric inference of causal effects of marketing effort. Our main insight is to use commonly observed discontinuities and kinks in the heuristics by which firms target such marketing activity to consumers for nonparametric identification. Such kinks, along with continuity restrictions that are typically satisfied in marketing and industrial organization applications, are sufficient for identification of local treatment effects. We review the theory of regression discontinuity estimation in the context of targeting and explore its applicability to several marketing settings. We discuss identifiability of causal marketing effects using the design and show that consideration of an underlying model of strategic consumer behavior reveals how identification hinges on model features such as the specification and value of structural parameters as well as belief structures. We emphasize the role of selection for identification. We present two empirical applications: the first measures the effect of casino e-mail promotions targeted to customers based on ranges of their expected profitability, and the second measures the effect of direct mail targeted by a business-to-consumer company to zip codes based on cutoffs of expected response. In both cases, we illustrate that exploiting the regression discontinuity design reveals negative effects of the marketing campaigns that would not have been uncovered using other approaches. Our results are nonparametric, easy to compute, and control for the endogeneity induced by the targeting rule.

Recent business research points to the fortune awaiting to be tapped in low-end markets. In this paper, we investigate how the size of the low-end market influences a firm's profits and the pioneering firm's quality choice. As low-valuation consumers increase in a market, on average, consumers' willingness to pay decreases. This may lead us to expect firms' profits to decrease as the size of the low-end market increases. Our analysis shows that, if the size of the low-end market is below a threshold, an increase in the size of the low-end market may actually dampen price competition and improve profits, as firms can then strategically choose their quality levels such that their products are more differentiated. Conventional wisdom also suggests that the pioneering firm will offer a higher-quality product and earn more profits compared with the later entrant. In contrast to this notion of quality advantage, our analysis identifies circumstances in which a pioneer can offer a lower-quality product and yet earn more profits. An experimental test lends support for some of our model's predictions. We further extend the model to consider markets with multiple firms, firms with multiple products, and consumers with limited purchasing power.

Recent research has empirically characterized the buyer–seller relationship as dynamically evolving from one discrete state to another. Conventional wisdom would suggest that a customer in a higher relationship state that has a higher transaction value would also have greater lifetime value to the firm. However, recent evidence suggests that higher relationship states can be ephemeral. Hence, the link between transaction value and lifetime value is not obvious. In this study, we seek to understand, within a specific empirical context, (i) the relationship between a customer's transaction value and that customer's lifetime value and (ii) the relationship between the lifetime value of a customer and the optimal level of marketing activity that needs to be directed at that customer. To this end, we develop a trivariate Tobit hidden Markov model that allows for (a) transitions among relationship states, (b) possible synergies between the various products that the supplier firm offers, (c) endogeneity in marketing activity, (d) heterogeneity in model parameters, and (e) the presence of the no-purchase option. Our results reinforce recent findings by Schweidel et al. [Schweidel, D. A., E. T. Bradlow, P. S. Fader. 2011. Portfolio dynamics for customers of a multiservice provider. Management Sci. 57(3) 471–486] that higher relationship states can be short-lived. Importantly for the supplier firm, a customer in the highest relationship state in a given period does not yield the highest lifetime value to the firm. Hence, the relationship between transaction value (i.e., relationship state) and lifetime value can be nonmonotonic. At the same time, we also find a nonmonotonic relationship between the optimal expenditures that should be directed at a customer and that customer's lifetime value; i.e., the optimal level of marketing contacts is not the highest for customers with the highest lifetime value. Furthermore, we find that the optimal marketing expenditures for myopic agents are 14%–33% lower than the corresponding values for forward-looking agents. Therefore, not accounting for the long-term effects of marketing contacts would lead to suboptimal marketing budgets. Moreover, a comparison with the current marketing expenditures suggests that the current practice is closer to the myopic policy than to the forward-looking one.

Online retailing provides an opportunity for new pricing options that are not feasible in traditional retail settings. This paper proposes an interactive, dynamic pricing strategy from the perspective of customized bundling to derive savings for customers while maximizing profits for electronic retailers (“e-tailers”). Given product costs, posted prices, shipping fees, and customers' reservation prices, we propose a nonlinear mixed-integer programming model to increase e-tailers' profits by sequentially pricing customized bundles. The model is flexible in terms of the number and variety of products customers may choose to incorporate during the various stages of their online shopping. Our computational study suggests that the proposed model not only attracts more customers to purchase the discounted bundle but also noticeably increases profits for e-tailers. This online dynamic bundle pricing model is robust under various bundle sizes and scenarios. It improves e-tailer profit and customer savings the most when facing divergent views about product values, lower budgets, and higher cost ratios.

The dramatic impact of the current crisis on performance of businesses across sectors and economies has been headlining the business press for the past several months. Extant reconciliations of these patterns in the popular press rely on ad hoc reasoning. Using historical data on currency crisis episodes across the world, we show that the impact of the crisis on a firm's business is best understood by focusing on the impact of the crisis on the behavior of consumers. Our analyses show that consumer behavior in a crisis is characterized by consumption smoothing at various levels—intertemporal, intercategory, and intracategory. These behavioral adjustments result in significant reallocation of consumption expenditures. More importantly, the smoothing decisions because of a crisis are distinct and independent of the impact of changes in income and prices that accompany a crisis. Interestingly, there is marked variation in the patterns of consumption smoothing across different types of economies. Taken together, these results have important and interesting implications for managers, policy makers, and academics.