Panel studies typically suffer from attrition, which reduces sample size and can result in biased inferences. It is impossible to know whether or not the attrition causes bias from the observed panel data alone. Refreshment samples-new, randomly sampled respondents given the questionnaire at the same time as a subsequent wave of the panel-offer information that can be used to diagnose and adjust for bias due to attrition. We review and bolster the case for the use of refreshment samples in panel studies. We include examples of both a fully Bayesian approach for analyzing the concatenated panel and refreshment data, and a multiple imputation approach for analyzing only the original panel. For the latter, we document a positive bias in the usual multiple imputation variance estimator. We present models appropriate for three waves and two refreshment samples, including nonterminal attrition. We illustrate the three-wave analysis using the 2007-2008 Associated Press-Yahoo! News Election Poll.

Television, the predominant advertising medium, is being transformed by the microtargeting capabilities of set-top boxes (STBs). By procuring impressions at the STB level (often denoted “programmatic television”), advertisers can now lower per-exposure costs and/or reach viewers most responsive to advertising creatives. Accordingly, this study uses a proprietary, household-level, single-source data set to develop an instantaneous show and advertisement viewing model to forecast consumers' exposure to advertising and the downstream consequences for impressions and sales. Viewing data suggest that person-specific factors dwarf brand- or show-specific factors in explaining advertising avoidance, thereby suggesting that device-level advertising targeting can be more effective than existing show-level targeting. Consistent with this observation, the model indicates that microtargeting lowers advertising costs and raises incremental profits considerably relative to show-level targeting. Further, these advantages are amplified when advertisers can buy in real time as opposed to up front.

T his 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.

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