By definition, mass advertising decisions cannot target local levels, which provides opportunities to identify the effect of advertising.

We collaborated with a large online social network to conduct a randomized field experiment measuring social ad effectiveness across 71 products in 25 categories among more than 37 million users.

This editorial introduces the special issue on marketing science and health. We begin by describing the healthcare ecosystem and its many distinguishing features relative to other markets. With its large share of U.S. and world gross domestic product; rapid changes on the demand, supply, and regulatory sides; and a complex ecosystem with many types of participants, healthcare markets provide a rich canvas of novel research opportunities for marketing scholars. We then describe the special issue process and the papers published in the special issue. We summarize key themes that emerge from these papers and conclude with a discussion of future research opportunities in the area.

To study consumer brand misinformation, we run in-store blind taste tests with a retailer’s private label food brands and the leading national brand counterparts in three large consumer packaged goods categories. Subjects self-report very high expectations about the quality of the private labels relative to national brands. However, they predict a relatively low probability of choosing them in a blind taste test. An overwhelming majority systematically choose the private label in the blinded test. Using program evaluation methods, we find that the causal effect of this intervention on treated consumers increases their market share for the tested private label product by 15 share points during the week after the intervention, on top of a base share of 8 share points. However, the effect diminishes to 8 share points during the second to fourth weeks after the test and to 2 share points during the second to fifth months after the test. Using a structural model of demand that controls for the self-selected participation and allows for heterogeneous treatment effects, we show that these effects survive controls for point-of-purchase prices, purchase incidence, and the feedback effects of brand loyalty. We also find that the intervention increases the preference for the private label brands, and that it decreases the preference for the national brands, relative to the outside good. Interpreting the intervention as an information treatment about the product, we find evidence consistent with an economically large informational barrier on demand for the private label product relative to an established national brand.

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 senior editors of Marketing Science are indebted to the many guest editors, 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, guest associate editors, and ad hoc reviewers who served from January 1, 2019, to December 31, 2019. Finally, our sincere appreciation to the authors, whose outstanding submissions and careful revisions make the journal the go-to resource for leading-edge knowledge in quantitative marketing. K. Sudhir Yale University

Victims from heavily minority areas complain to the government much less than those from nonminority areas, relative to their degree of victimization.

Consumers often learn the weights they ascribe to product attributes (“preference weights”) as they search. For example, after test driving cars, a consumer might find that he or she undervalued trunk space and overvalued sunroofs. Preference-weight learning makes optimal search complex because each time a product is searched, updated preference weights affect the expected utility of all products and the value of subsequent optimal search. Product recommendations, which take preference-weight learning into account, help consumers search. We motivate a model in which consumers learn (update) their preference weights. When consumers learn preference weights, it may not be optimal to recommend the product with the highest option value, as in most search models, or the product most likely to be chosen, as in traditional recommendation systems. Recommendations are improved if consumers are encouraged to search products with diverse attribute levels, products that are undervalued, or products for which recommendation-system priors differ from consumers’ priors. Synthetic data experiments demonstrate that proposed recommendation systems outperform benchmark recommendation systems, especially when consumers are novices and when recommendation systems have good priors. We demonstrate empirically that consumers learn preference weights during search, that recommendation systems can predict changes, and that a proposed recommendation system encourages learning.The data files and online appendix are available at https://doi.org/10.1287/mksc.2018.1144 .