Sundar Dorai-Raj
I received a Ph.D. in Statistics from Virginia Tech in 2001. After spending 7 years designing and analyzing experiments for the semiconductor industry, I joined Google's TV Ads team. My current focus is on video ads quality and effectiveness on YouTube.
Authored Publications
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Empowering Online Advertisements by Empowering Viewers with the Right to Choose
Melanie Kellar
Dan Zigmond
Journal of Advertising Research, 52 (2012), pp. 65-71
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In 2010, YouTube introduced TrueView in-stream advertising—online video advertisements that allowed the user to skip directly to the desired video content after five seconds of
viewing. Google sought to compare these “skippable” in-stream advertisements to the conventional (non-skippable) in-stream video advertising formats, using a new advertising
effectiveness metric based on the propensity to search for terms related to advertising content. Google’s findings indicated that skippable video advertisements may be as
effective on a per-impression basis as traditional video advertisements. In addition, data from randomized experiments showed a strong implied viewer preference for the skippable advertisements. Taken together, these results suggest that formats like TrueView in-stream advertisements can improve the viewing experience for users without sacrificing advertising value for advertisers or content owners.
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Advertising and Traffic: Learning from online video data
Dan Zigmond
Audience Measurement 6.0, Advertising Research Foundation, New York, NY (2011)
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Online media portals like Google’s YouTube are generating unprecedented volumes of data on usage patterns and viewing behavior. Learn about improving online advertising by understanding how ads impact online traffic.
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Measuring the Impact of Advertising on YouTube Traffic
The Market Research Event, The Market Research Event, Orlando FL (2011)
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At YouTube, balancing ad load and user happiness is a major concern. One way we measure this tradeoff is through live experiments, which we run on a small percentage of traffic. For example, by holding back certain ad formats we can build metrics around the impact of YouTube advertising on the user experience. In this talk we will discuss the benefits and challenges of running large-scale advertising experiments.
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Adapting Online Advertising Techniques to Television
Yannet Interian
Igor Naverniouk
Dan Zigmond
Online Multimedia Advertising: Techniques and Technologies, Information Science Reference, Hershey PA (2011), pp. 148-165
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The availability of precise data on TV ad consumption fundamentally changes this advertising medium, and allows many techniques developed for analyzing online ads to be adapted for TV. This chapter looks in particular at how results from the emerging field of online ad quality analysis can now be applied to TV.
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Google has developed new metrics based on set-top box data for predicting the future audience retention of TV ads. This paper examines how to use these metrics to judge the effectiveness of TV ad campaigns. More specifically, we analyze how these metrics can inform future campaign targeting and placement goals.
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For several years, Google has been analyzing television set-top box data to measure audience response to specific TV ads. This paper presents how similar techniques can be applied to online video advertising on YouTube. As more and more video programming is made available online, it will become increasingly important to understand how to engage with online viewers through video advertising. Furthermore, we find that viewing behavior is even more effected by specific video ad creatives online than it is on TV. This suggests that online viewing can become a valuable source data on viewer response to video ad creatives more generally.
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Ad Quality On TV: Predicting Television Audience Retention
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Yannet Interian
Igor Naverniouk
P. J. Opalinski
Kaustuv
Dan Zigmond
Proceedings of ADKDD (2009)
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The sos package provides a means to quickly and flexibly search the help pages of contributed packages, finding
functions and datasets in seconds or minutes that could not be found in hours or days by any other means we know. Its findFn function accesses Jonathan Baron's R Site Search database and returns the matches in a data frame of class "findFn", which can be further manipulated by other sos functions to produce, for example, an Excel file that starts with a summary sheet that makes it relatively easy to prioritize alternative packages for further study. As such, it provides a very powerful way to do a literature search for functions and packages relevant to a particular topic of interest and could become virtually mandatory for authors of new packages or papers in publications such as The R Journal and the Journal of Statistical Software.
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Measuring Advertising Quality on Television: Deriving Meaningful Metrics from Audience Retention Data
Dan Zigmond
Yannet Interian
Igor Naverniouk
Journal of Advertising Research, 49 (2009), pp. 419-428
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This paper introduces a measure of television ad quality based on audience retention, using logistic regression techniques to normalize such scores against expected audience behavior. By adjusting for features such as time of day, network, recent user behavior, and household demographics, we are able to isolate ad quality from these extraneous factors. We introduce the current model used in our production system, as well as two new competing models that show some improvement. We also devise metrics for calculating a model’s predictive power and variance, allowing us to determine which of our models performs best. We conclude with discussions of retention score applications for advertisers to evaluate their ad strategies, and potential as an aid in future ad pricing.
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