Browsing by Author "Deng, Yiting"

Television (TV) is the predominant advertising medium, and recent technological advances such as digital video recorders (DVRs) and set-top boxes (STBs) have the potential to transform this industry by enabling household-specific advertising. Since exposure to TV represents a substantial share of consumer time and attention, this potential to micro-target communications represents an enormous opportunity for the TV advertising market.

This paper outlines an approach to facilitate the micro-targeting of TV advertising. We employ a unique dataset, integrating TV program and advertisement viewing at the household level with purchase data, to address the question of how advertisers can achieve better advertising targeting in the digital context. Based on this dataset, we first develop a model of household TV viewing behavior. The viewing model comprises three integrated components: TV show sampling and watching, TV show recording, and advertising viewing. All three components are motivated by the theoretical concept of flow utility, that is, the moment-by-moment enjoyment a household derives from different activities: watching a TV show, watching a TV advertisement, and other non-TV activities. This model has decent out-of-sample prediction power on show choices and time spent on each selected show. We then link household advertising exposure with purchase. Finally, the viewing model and identified advertising-sales relationship are utilized to conduct counterfactual policy experiments on advertising targeting. We consider several household-level targeting scenarios by manipulating: 1) whether the advertising purchase is made in advance; and 2) whether the objective function is to minimize costs for a given set of exposures or to maximize revenues from advertising. Results indicate micro-targeting can lower advertising costs and raise incremental revenue.

The key contributions of this paper are as follows. Theoretically, we develop an integrated model on TV show viewing, TV advertising viewing, purchasing and advertising targeting. Methodologically, we propose a new modeling framework on media consumption by explicitly accounting for the role of uncertainty, and propose targeting strategies leveraging household-level data. Substantively, we offer policy recommendations to advertisers on micro-targeting which can be of great potential.

Most panel surveys are subject to missing data problems caused by panel attrition. The Additive Non-ignorable (AN) model proposed by Hirano et al. (2001) utilizes refreshment samples in panel surveys to impute missing data, and offers flexibility in modeling the missing data mechanism to incorporate both ignorable and non-ignorable models. We extend the AN model to settings with three waves and two refreshment samples. We address identication and estimation issues related to the proposed model under four different types of survey design, featured by whether the missingness is monotone and whether subjects in the refreshment samples are followed up in subsequent waves of the survey. We apply this approach and multiple imputation techniques to the 2007-2008 Associated Press-Yahoo! News Poll (APYN) panel dataset to analyze factors affecting people's political interest. We find that, when attrition bias is not accounted for, the carry-on effects of past political interest on current political interest are underestimated. This highlights the importance of dealing with attrition bias and the potential of refreshment samples for doing so.