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Abstract:

Advertisement-supported media models are built upon an implicit agreement that media publishers make with their audience: the price of free or subsidized content is the audience’s attention to advertisements. A key component in the evaluation of an advertiser’s return on investment is whether an advertisement held the audience’s attention well enough to generate a conversion (e.g., a sale, website/store visit, newsletter sign-up). In our modern era of near-constant access to information, arguably one of the greatest threats to audience attention is information overload. This threat is likely to increase in severity as ad content increases in quantity. Recent research by Abcarian and Rao (in progress) has demonstrated an increase in the number of ad spots generated by media companies over time. How do audiences react to increased ad exposure? Is there an optimal ad duration that elicits higher levels of audience attention? This study seeks to answer these questions. This study adds to the growing body of research on audience attention in the context of ad exposure measurement. Preliminary results indicate that shorter-duration ads (15 seconds) are more effective in garnering audience attention than ads of longer duration (30+ seconds). Our paper includes a more extensive discussion of our findings along with recommendations for future research.

Recommended Citation:

Nandi, S., Craft, M., & Rao, K. (2023). Is Less the Merrier for Ad Exposure and Audience Attention? A Media Measurement Case Study. American Association for Public Opinion Research, Philadelphia, PA.

Attached Documents/Links:

• AAPOR 2023 Program
• For a copy of this presentation, please send me a comment with your email address in the box below or an email to kumarrons at gmail dot com.

Abstract:

Consumer-file marketing companies collect and sell customer’s demographic and behavioral information wherever they can: through loyalty cards, website visits, social media posts, etc. These ancillary data are tremendously valuable and have a variety of applications in the field of television and digital media measurement that include targeting households or individuals for marketing campaigns, adjusting for nonresponse bias, and projecting estimates from a sample to an underlying universe. These potential advantages, however, depend on the completeness of the information contained in the data. Today, most applications involving the use of ancillary data exclude incomplete records from the substantive analysis. Exclusion as a missing data handling strategy has been known to result in inaccurate conclusions depending on the nature of the mechanism assumed to have generated the missing data. In this paper, we demonstrate a model-based alternative to exclusion, called multiple imputation (MI), which is used to fill in the missing values. A correctly specified imputation model preserves important characteristics of the data (e.g., variances, correlations, interactions) and can mitigate any bias resulting from exclusion-based missing data handling strategies. Our paper demonstrates MI in the context of a substantive analysis of the relationship between a completely observed measure of the number of advertisement impressions served across television networks and an incomplete measure of the number of children per household. We include likely correlates of the suspected cause of the missing data in the imputation model to evaluate whether their inclusion improves the quality of the imputed values. These correlates are measures pulled from a second, more completely observed, ancillary dataset containing television and digital media audience measurements. This paper concludes with a discussion of our findings, which are likely to have important implications for the use of ancillary data in television and digital media audience measurement.

Recommended Citation:

Craft, M., Raghunath, A., & Rao, K. (2023). Does Ancillary Data about Ancillary Data Help Treat Missing Responses in Ancillary Data? American Association for Public Opinion Research, Philadelphia, PA.

Attached Documents/Links:

• AAPOR 2023 Program
• For a copy of this presentation, please send me a comment with your email address in the box below or an email to kumarrons at gmail dot com.

Abstract:

The new reality for consumers is they not only have access to more content than ever before, but they can also select the content they want, when they want, and watch in the device they want. One such device that has become increasingly popular for media consumption is Over-the-Top (OTT) media players. These are devices that deliver video content via the internet to television sets. Today, there exists an ever-growing number of various OTT devices from Roku players, the Apple TV, the Amazon Fire TV box, Chromecast, and game consoles. However, with this increased availability of choice comes the growing fragmentation of consumer time and attention. This leaves advertisers with the complex task of breaking through the clutter of advertisements and finding a way to reach the OTT device-specific audience. However, reaching an audience behind an OTT device requires a thorough understanding of the viewers. To date, there has been no study examining the differences between various types of OTT device owners and their viewing behaviors. Read more

Abstract:

Over the past few years, we have witnessed an expanding range of viewing devices and new content offerings by online streaming services (such as Netflix, Amazon and Hulu) through over-the-top (OTT) devices. Nearly 20% of U.S. households own at least one OTT device, such as a Roku, Amazon Fire TV, or Apple TV (Park Associates 2015). As these trends keep increasing, there have been debates on whether online streaming will replace traditional (or cable) TV in near future. Furthermore, questions have been raised around whether OTT viewing, via Apps, is cannibalizing or complementing network oriented TV viewing. Does multiple layers of ownership/access (ex: device, App, etc.) in OTT viewing play a role in their viewing/usage behavior to be different from traditional TV viewing? Does these two forms of TV viewership different in terms of types of programs watched, when they are watched, and how often they are watched? These are all questions of great importance to online publishers and advertisers, and, in general, to researchers working with large volume and variety of TV viewing data. Answering these questions is at the heart of this study and analysis. Read more

Abstract:

The number of US households with access to computers at home has continued to grow. According to the 2011 Computer and Internet Use report published by US department of Commerce, 77% of US homes have computers in their home, compared to 62% in 2003. Many households, however, do not have multiple computers dedicated to each member living in the house. As such, sharing of computers amongst household members can be a prevalent phenomenon in home computer usage. Understanding this within-house computer sharing phenomenon and identifying the mostly likely person behind the computer screen can be of interest to market researchers and practitioners, particularly those interested in studying effective ways to target online ads based on users, online activities. For survey researchers who are attempting to recruit hard-to-reach individuals like teens and young adults, understanding of computer sharing could help establish contact at times when those individuals are more likely to be behind the computer. Despite its prevalence, within-house computer sharing has barely received any research attention. This study hopes to break through the barriers preventing the light of scientific inquiry into this phenomenon. Read more