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Viewing time as a cross-media metric: Comparing viewing time for video advertising on television and online

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Abstract

The Media Rating Council recommends weighting advertising exposure by viewing time. Prior research shows viewing time has diminishing returns, implying that effectiveness equivalent to a 100% complete exposure could be achieved by a lower threshold. Results from four laboratory experiments, extending prior banner-ad research to dynamic video ads, suggest 75% viewed is a potential threshold. A second contribution identifies different viewing time distributions for television and online video, due to differences in ad avoidance. More television ads have viewing times exceeding the 75% threshold, and so are more effective than the typical online video ad. Online networks could charge fees equivalent to television ads for video ads that exceed the 75% threshold. A third contribution is the use of interval outcome estimation (IOE), which revealed asymmetric effects of viewing time and that brand familiarity rather than viewing time is the only necessary explanation of ad effectiveness measured by recall.

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... Uhl et al. [136] did something similar by analyzing view-through rates for various viewability criteria to quantify the causal effect of viewability. Also, Bellman et al. [14] explored several video completion thresholds to be used to detect viewability in video ads. However, according to [1,36,54,99], these empirical studies might be biased by the experimental design used to obtain the results (like the display size used by the users to access the websites, the browsing settings, or the ad format selected) or the user perception towards the website and the advertised products. ...
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We develop a conceptual framework about the impact that branding activity (the audiovisual representation of brands) and consumers' focused versus dispersed attention have on consumer moment-to-moment avoidance decisions during television advertising. We formalize this framework in a dynamic probit model and estimate it with Markov chain Monte Carlo methods. Data on avoidance through zapping, along with eye tracking on 31 commercials for nearly 2,000 participants, are used to calibrate the model. New, simple metrics of attention dispersion are shown to strongly predict avoidance. Independent of this, central on-screen brand positions, but not brand size, further promote commercial avoidance. Based on the model estimation, we optimize the branding activity that is under marketing control for ads in the sample to reduce commercial avoidance. This reveals that brand pulsing--while keeping total brand exposure constant--decreases commercial avoidance significantly. Both numerical simulations and a controlled experiment using regular and edited commercials, respectively, provide evidence of the benefits of brand pulsing to ward off commercial avoidance. Implications for advertising management and theory are addressed.