Knowledge that Transforms

Marketing Science greatly benefited from the admirable and fastidious efforts of more than 200 different individuals who provided manuscript reviews last year. Beyond those individuals already recognized on the editorial board, the editor-in-chief and guest editors of Marketing Science are indebted to the many guest editors-in-chief, guest associate editors, and ad hoc reviewers who provided expert counsel and guidance on a voluntary basis. The following list acknowledges the contribution of guest editors-in-chief, guest associate editors, and ad hoc reviewers who served from January 1, 2013 to December 31, 2013. Finally, let us not forget to thank the authors. Marketing Science requires and receives outstanding submissions from many leading researchers and prestigious organizations. Preyas S. Desai Duke University

In this paper, we untangle the searchable and experiential dimensions of quality responses to entry by counterfeiters in emerging markets with weak intellectual property rights. Our theoretical framework analyzes market equilibria under competition from counterfeiting as well as under monopoly branding. A key theoretical prediction is that emerging markets can be self-corrective with respect to counterfeiting issues in the following sense: First, counterfeiters can earn positive profits by pooling with authentic brands only when consumers have good faith in the market (i.e., they believe there is low probability that any product is a counterfeit). When the proportion of counterfeits in the market exceeds a cutoff value, brands invest in self-differentiation from the competitive-fringe counterfeiters. Second, to attain a separating equilibrium with counterfeiters, branded incumbents upgrade the searchable quality (e.g., appearance) of their products more and improve the experiential quality (e.g., functionality) less compared with monopoly equilibrium. However, in the pooling equilibrium with sporadic counterfeits, authentic firms instead may invest in experiential quality to attract more of the expert consumers who are well versed in quality. This prediction uncovers the nature of product differentiation in the searchable dimension and helps with analyzing real-world innovation strategies employed by authentic firms in response to entries by counterfeit entities. In addition, welfare analysis hints at a nonlinear relationship between social welfare and intellectual property enforcement.

Many video ads are designed to go viral so that the total number of views they receive depends on customers sharing the ads with their friends. This paper explores the relationship between the number of views and how persuasive the ad is at convincing consumers to purchase or to adopt a favorable attitude towards the product. The analysis combines data on the total views of 400 video ads, and crowd-sourced measurement of advertising persuasiveness among 24,000 survey responses. Persuasiveness is measured by randomly exposing half of these consumers to a video ad and half to a similar placebo video ad, and then surveying their attitudes towards the focal product. Relative ad persuasiveness is on average 10% lower for every one million views that the video ad achieves. The exceptions to this pattern were ads that generated views and large numbers of comments, and video ads that attracted comments that mentioned the product by name. Evidence suggests that such ads remained effective because they attracted views due to humor rather than because they were outrageous.

The customer relationship management allocation in marketing budgets is potentially misleading when it uses individual customer lifetime value estimations from historical data. Planned marketing interventions would change the purchasing behavior of different customers, and history-based decisions would thus be suboptimal. To cope with this inherent endogeneity, we model the optimal allocation of the marketing mix by accounting simultaneously for mass interventions and direct marketing interventions for each customer. This is a large stochastic dynamic problem that, in general, is computationally rather intractable as a result of the “curse of dimensionality.” We present an algorithm to derive the optimal marketing policies (how the firm should allocate its marketing resources) and the expected present value of those decisions, which maximize the long-term profitability of firms. This allows the firm to value customers/segments and helps the firm to target those that maximize long-term profitability given the optimal marketing resources allocation. We apply the proposed approach in the context of a kitchen appliance manufacturer. The results identify the most effective marketing policies and the endogenous customer values. It is in this context that we also dynamically identify the most profitable customer and the short- and long-term effects of marketing activities on each customer.

Multipart tariffs are widely favored within service industries as an efficient means of mapping prices to differential levels of consumer demand. Whether they benefit consumers, however, is far less clear as they pose individuals with a potentially difficult task of dynamically allocating usage over the course of each billing cycle. In this paper we explore this welfare issue by examining the ability of individuals to optimally allocate consumption over time in a stylized cellular-phone usage task for which there exists a known optimal dynamic utilization policy. Actual call behavior over time is modeled using a dynamic choice model that allows decision makers to both discount the future (be myopic) and be subject to random errors when making call decisions. Our analysis provides a “half empty, half full” view of intuitive optimality. Participants rapidly learn to exhibit farsightedness, yet learning is incomplete with some level of allocation errors persisting even after repeated experience. We also find evidence for an asymmetric effect in which participants who are exogenously switched from a low (high) to high (low) allowance plan make more (fewer) errors in the new plan. The effect persists even when participants make their own plan choices. Finally, interventions that provide usage information to help participants eradicate errors have limited effectiveness.

When managers and researchers encounter a data set, they typically ask two key questions: (1) Which model (from a candidate set) should I use? And (2) if I use a particular model, when is it going to likely work well for my business goal? This research addresses those two questions and provides a rule, i.e., a decision tree, for data analysts to portend the “winning model” before having to fit any of them for longitudinal incidence data. We characterize data sets based on managerially relevant (and easy-to-compute) summary statistics, and we use classification techniques from machine learning to provide a decision tree that recommends when to use which model. By doing the “legwork” of obtaining this decision tree for model selection, we provide a time-saving tool to analysts. We illustrate this method for a common marketing problem (i.e., forecasting repeat purchasing incidence for a cohort of new customers) and demonstrate the method's ability to discriminate among an integrated family of a hidden Markov model (HMM) and its constrained variants. We observe a strong ability for data set characteristics to guide the choice of the most appropriate model, and we observe that some model features (e.g., the “back-and-forth” migration between latent states) are more important to accommodate than are others (e.g., the inclusion of an “off” state with no activity). We also demonstrate the method's broad potential by providing a general “recipe” for researchers to replicate this kind of model classification task in other managerial contexts (outside of repeat purchasing incidence data and the HMM framework).

There is substantial academic interest in modeling consumer experiential learning. However, (approximately) optimal solutions to forward-looking experiential learning problems are complex, limiting their behavioral plausibility and empirical feasibility. We propose that consumers use cognitively simple heuristic strategies. We explore one viable heuristic—index strategies—and demonstrate that they are intuitive, tractable, and plausible. Index strategies are much simpler for consumers to use but provide close-to-optimal utility. They also avoid exponential growth in computational complexity, enabling researchers to study learning models in more complex situations. Well-defined index strategies depend on a structural property called indexability. We prove the indexability of a canonical forward-looking experiential learning model in which consumers learn brand quality while facing random utility shocks. Following an index strategy, consumers develop an index for each brand separately and choose the brand with the highest index. Using synthetic data, we demonstrate that an index strategy achieves nearly optimal utility at substantially lower computational costs. Using IRI data for diapers, we find that an index strategy performs as well as an approximately optimal solution and better than myopic learning. We extend the analysis to incorporate risk aversion, other cognitively simple heuristics, heterogeneous foresight, and an alternative specification of brands.

Marketing managers often use consumer attitude metrics such as awareness, consideration, and preference as performance indicators because they represent their brand's health and are readily connected to marketing activity. However, this does not mean that financially focused executives know how such metrics translate into sales performance, which would allow them to make beneficial marketing mix decisions. We propose four criteria—potential, responsiveness, stickiness, and sales conversion—that determine the connection between marketing actions, attitudinal metrics, and sales outcomes. We test our approach with a rich data set of four-weekly marketing actions, attitude metrics, and sales for several consumer brands in four categories over a seven-year period. The results quantify how marketing actions affect sales performance through their differential impact on attitudinal metrics, as captured by our proposed criteria. We find that marketing–attitude and attitude–sales relationships are predominantly stable over time but differ substantially across brands and product categories. We also establish that combining marketing and attitudinal metrics criteria improves the prediction of brand sales performance, often substantially so. Based on these insights, we provide specific recommendations on improving the marketing mix for different brands, and we validate them in a holdout sample. For managers and researchers alike, our criteria offer a verifiable explanation for differences in marketing elasticities and an actionable connection between marketing and financial performance metrics.