Knowledge that Transforms

In their article, Ho, Lim, and Camerer (2006) lead by example. They identify principles from behavioral economics, and rather than simply exhort their readers to pay attention, they actually produce impressive demonstrations of how these principles can be applied to substantive marketing problems. It is easy to argue that these ideas are important, but it is more difficult to demonstrate that importance. Implicitly, Ho, Lim, and Camerer send a message of encouragement: Behaviorally realistic assumptions are not problems to be ignored; they are opportunities. Formal models can capture psychologically realistic concerns. Together, they should be the grist for the next generation of marketing mills.

Several studies show that information used to screen alternatives becomes less important than information acquired later in the search process simply because it was used to screen. Experiment 1 shows that the tendency to deemphasize prescreening information leads to systematically different choices for decision makers who screen alternatives compared with decision makers who do not screen alternatives. Additional studies show that screening encourages decision makers to shift their emphasis from prescreening information to postscreening information (Experiment 2). Prescreening information is deemphasized because of the categorization that occurs when people create a consideration set of retained alternatives (Experiments 3 and 4). Together, the results show that a brand's strength of consideration (i.e., how highly an option ranks on screening criteria) may have little influence on the likelihood of it being chosen in a postscreening choice process.

This research presents a feature-based statistical model and subsequently explores the degree to which similarity perceptions between two advertisements can be decomposed and explained by a “weighted-and-summed” distance measure, computed on the advertisements' executional elements, after controlling for familiarity and viewers' attitudinal responses toward the advertisements. Furthermore, the authors obtain empirical findings in two major areas: First, variation in similarity ratings can be explained by the advertisements' features, a finding of potential importance for advertisement construction. Second, some, but not all, executional elements that have been shown (in the literature) to drive recall and persuasion are effective at driving perceptions of similarity. This is of practical importance because managers want their advertisements not only to be liked and remembered but also (possibly) to be perceived as similar (or dissimilar) to those for other products. In particular, an understanding of which items drive which constructs (recall and persuasion, or similarity) can contribute to a more effective overall marketing strategy.

This work examines consumers' preferences for consumption timing. Specifically, the authors examine how temporal framing (deferring versus expediting) of a decision moderates the sensitivity of consumers' pattern of discounting to changes in time horizon. The results from three experiments show greater decline in consumers' discount rates with time horizon (i.e., greater present bias) when they defer than when they expedite consumption. These results are robust to using monetary and nonmonetary outcomes, as well as to different time horizons (months, days). The authors further demonstrate that the different levels of mental representations (concreteness) triggered by the two decision frames moderate this difference in sensitivity.

In this era of increasing obesity and increasing threats of legislation and regulation of food marketing practices, regulatory agencies have pointedly asked how “low-fat” nutrition claims may influence food consumption. The authors develop and test a framework that contends that low-fat nutrition labels increase food intake by (1) increasing perceptions of the appropriate serving size and (2) decreasing consumption guilt. Three studies show that low-fat labels lead all consumers—particularly those who are overweight—to overeat snack foods. Furthermore, salient objective serving-size information (e.g., “Contains 2 Servings”) reduces overeating among guilt-prone, normal-weight consumers but not among overweight consumers. With consumer welfare and corporate profitability in mind, the authors suggest win-win packaging and labeling insights for public policy officials and food marketers.

Regulatory fit occurs when the manner of peoples' engagement in an activity sustains their current goal orientation or concerns with that activity. It is proposed that regulatory fit changes the significance of consumers' reactions to something, including the perceived monetary value of a choice they have made or the persuasiveness of a message they have received. When there is a fit, people engage more strongly in and “feel right” about what they are doing, and subsequent evaluative reactions (positive or negative) can be intensified by this fit experience. The fit experience is shown to influence the strength of value experiences independent of hedonic experiences. The authors discuss how the fit effect on value is distinct from other factors that affect value, such as relevancy, matching, hedonic mood, and arousal. Using prior research on this topic, this article summarizes the current state of knowledge about how fit influences value and offers new ideas for further research.

A common assumption underlying the analysis of consumers' choices among optional tariffs is that consumers choose the tariff that maximizes their surplus and, thus, the tariff that leads to the lowest billing rate for a given amount of usage. Yet there is evidence that many users prefer a flat rate even though their billing rate would be lower with a pay-per-use tariff (flat-rate bias), and some users prefer a pay-per-use tariff even though they would save money with a flat rate (pay-per-use bias). The authors conduct four empirical analyses based on three different data sets. They show that the flat-rate bias is more important and has a greater regularity and time persistence than the pay-per-use bias. They classify potential causes of the flat-rate bias as “insurance effect,” “taxi meter effect,” “convenience effect,” and “overestimation effect” and show that the insurance, the taxi meter, and the overestimation effects lead to a flat-rate bias. They provide evidence that underestimation of usage is a major cause of the pay-per-use bias. They show that the flat-rate bias does not significantly increase customer churn and thus results in a short- and long-term profit increase. In contrast, the pay-per-use bias largely increases churn so that in the long run, the additional short-term profit is offset by higher churn.

Preference heterogeneity is a major research stream in marketing aimed at quantifying and understanding the diversity of demand for product attributes and attribute levels. In experimental settings, in which consumers are presented with simple descriptions of product offerings, continuous distributions of heterogeneity, such as the multivariate normal, provide a useful representation of preference. However, in more complex cases in which respondents have value for only a few of the benefits associated with an offering or cognitive constraints that result in selective attention to a subset of the information available, continuous distributions of heterogeneity do not reflect the possibility that a subset of the variables has nonzero effect sizes for different respondents. Identifying which attributes are used in a brand choice decision is closely related to the statistical procedure of variable selection. This article extends variable selection methods to accommodate heterogeneity across consumers and data contexts, conditions frequently encountered in marketing studies. The authors apply the methods to a discrete-choice conjoint study in which data are collected in both full-profile and partial-profile formats.

This article provides a descriptive analysis of how methodological factors contribute to the accuracy of customer churn predictive models. The study is based on a tournament in which both academics and practitioners downloaded data from a publicly available Web site, estimated a model, and made predictions on two validation databases. The results suggest several important findings. First, methods do matter. The differences observed in predictive accuracy across submissions could change the profitability of a churn management campaign by hundreds of thousands of dollars. Second, models have staying power. They suffer very little decrease in performance if they are used to predict churn for a database compiled three months after the calibration data. Third, researchers use a variety of modeling “approaches,” characterized by variables such as estimation technique, variable selection procedure, number of variables included, and time allocated to steps in the model-building process. The authors find important differences in performance among these approaches and discuss implications for both researchers and practitioners.

In this article, the authors explore the bagging and boosting classification techniques. They apply the two techniques to a customer database of an anonymous U.S. wireless telecommunications company, and both significantly improve accuracy in predicting churn. This higher predictive performance could ultimately lead to incremental profits for companies that use these methods. Furthermore, the results recommend the use of a balanced sampling scheme when predicting a rare event from large data sets, but this requires an appropriate bias correction.