Rachel Fan
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Co-viewing refers to the situation in which multiple people share the experience of watching video content and ads in the same room and at the same time. In this paper, we use online surveys to measure the co-viewing rate for YouTube videos that are watched on a TV screen. These simple one-question surveys are designed to optimize response accuracy. Our analysis of survey results identifies variations in co-viewing rate with respect to important factors that include the demographic group (age/gender) of the primary viewer, time of day, and the genre of the video content. Additionally, we fit a model based on these covariates to predict the co-viewing rate for ad impressions that are not directly informed by a survey response. We present results from a case study and show how co-viewing changes across these covariates.
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Many advertisers rely on attribution to make a variety of tactical and strategic marketing decisions, and there is no shortage of attribution models for advertisers to consider. In the end, most advertisers choose an attribution model based on their preconceived notions about how attribution credit should be allocated. A misguided selection can lead an advertiser to use erroneous information in making marketing decisions. In this paper, we address this issue by identifying a well-defined objective for attribution modeling and proposing a systematic approach for evaluating and comparing attribution model performance using simulation. Following this process also leads to a better understanding of the conditions under which attribution models are able to provide useful and reliable information for advertisers.
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Methods for Measuring Brand Lift of Online Ads
Tim Hesterberg
Ying Liu
Lu Zhang
Proceedings of the 2018 Joint Statistical Meetings, American Statistical Association
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We describe ways to measure ad effectiveness for brand advertisements using online surveys. We estimate the causal effect of ads using randomized experiments. We focus on some technical issues that arise with imperfect A/B experiments--corrections for solicitation and response bias in surveys, discrepancies between intended and actual treatment, and comparing treatment group users who took an action with control users who might have acted. We discuss different methods for estimating lift for different slices of the population, to achieve different goals. We use regression, with a particular form of regularization that is particularly suited to this application. We bootstrap to obtain standard errors, and compare bootstrap methods.
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Brand Attitudes and Search Engine Queries
Jeffrey P. Dotson
Elea McDonnell Feit
Yi-Hsin Yeh
Journal of Interactive Marketing, vol. 37 (2017), pp. 105-116 (to appear)
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Search engines record the queries that users submit, including a large number of queries that include brand names. This data holds promise for assessing brand health. However, before adopting brand search volume as a brand metric, marketers should understand how brand search relates to traditional survey-based measures of brand attitudes, which have been shown to be predictive of sales. We investigate the relationship between brand attitudes and search engine queries using a unique micro-level data set collected from a panel of Google users who agreed to allow us to track their individual brand search behavior over eight weeks and link this search history to their responses to a brand attitude survey. Focusing on the smartphone and automotive markets, we find that users who are actively shopping in a category are more likely to search for any brand. Further, as users move from being aware of a brand to intending to purchase a brand, they are increasingly more likely to search for that brand, with the greatest gains as customers go from recognition to familiarity and from familiarity to consideration. Additionally, users that own and use a particular automotive or smartphone brand are much more likely to search for that brand, even when they are not in market suggesting that a substantial volume of brand search in these categories is not related to shopping or product search. We discuss the implications of these findings for assessing brand health from search data.
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