Sundar Dorai-Raj’s research while affiliated with Google Inc. and other places

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.

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.

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 func- tions to produce, for example, an Excel file that starts with a summary sheet that makes it rela- tively easy to prioritize alternative packages for further study. As such, it provides a very power- ful way to do a literature search for functions and packages relevant to a particular topic of interest and could become virtually mandatory for au- thors of new packages or papers in publications such as The R Journal and the Journal of Statistical Software.

This paper explores the impact of television advertisements on audience retention using data collected from television set-top boxes (STBs)1. In particular, we discuss how the accuracy of the retention score, a measure of ad quality, is improved by using the recent "click history" of the STBs tuned to the ad. These retention scores are related to - and are a natural extension of - other measures of ad quality that have been used in online advertising since at least 2005 (2). Like their online counterparts, TV retention scores could be used to determine if an ad should be eligible to enter the inventory auction and, if it is, how highly the ad should be ranked (1). A retention score (RS) could also be used by the auction system for pricing, or by the advertiser to compare different creatives for the same product.

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. Introduction In recent years, there has been an explosion of interest in collecting and analyzing television set-top box (STB) data (also called "return path" data). As US television moves from analog to digital signals, digital set-top boxes are increasingly common in American homes. Where these STBs are attached to some sort of return path, this data can be aggregated and licensed to companies wishing to measure television viewership. For example, Google aggregates data, collected and anonymized by DISH Network L.L.C., describing the precise second-by-second tuning behavior from television set-top boxes in millions of US households. This data can be combined with detailed airing logs for thousands of daily TV ads to estimate second-by-second fluctuations in audience during TV commercials (Zigmond and Lanning, 2008). These data hold the promise of providing accurate measurement for much of the niche TV content that eludes current panel-based methods. But in addition to using these data for raw audience measurement, it is possible to make more qualitative judgments about the content – and specifically the advertising – on television. Google has developed a measure of audience retention based on STB data that can be used to predict future audience response for TV ads (Zigmond, 2009a and Zigmond et al, 2009b).