Archive for the ‘ Conference Articles ’ Category

Is Less the Merrier for Ad Exposure and Audience Attention? A Media Measurement Case Study

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.

Latent Class Analysis: A Probabilistic Approach to Uncovering Latent Classes of TV Advertisements

Abstract:

In media measurement, examinations of relationships between audience behaviors (e.g., viewership of TV advertisements) and demographics (e.g., age and gender) are common practice. These examinations can follow a variable-centered approach that differentiates variables into dependent and independent variables (e.g., linear regression) or a person-centered approach which groups observations together according to their configural profile of characteristics (e.g., K-means; Morris & Kavussanu, 2008). This paper extends the boundaries of media research by using a latent clustering method — Latent Class Analysis (LCA) for analyzing advertisement viewership data. LCA is a probabilistic mixture modeling technique used to identify a set of discrete latent classes of observations, in this case advertisements, based on the values of a set of categorical indicators. The technique focuses on generating classes of advertisements that share similar patterns of responses and comparing with other classes to subsequently determine how they are differentially related to predictors and outcomes (Wang & Hanges, 2011).

The dataset used in our analysis is characterized by extreme volume (7M rows) and a variety of variable types (variables are a combination of continuous, categorical, and count). Preliminary findings indicate the classification of advertisements into two latent classes as defined by a set of categorical indicators of interest. The most salient features of Class 1 in comparison to Class 2 are a greater likelihood to air within News/Information and Sports TV genres as well as a greater likelihood to air on a weekday as opposed to a weekend or weeknight. We conduct additional analyses to understand how these latent classifications are related to a measure of what the media world refers to as co-viewing. This paper concludes with a discussion of the results and their implications along with recommendations for future research. R code and a sample dataset is provided for interested readers and practitioners.

Recommended Citation:

Craft, M., & Rao, K. (2023). Latent Class Analysis: A Probabilistic Approach to Uncovering Latent Classes of TV Advertisements. 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.

Does Ancillary Data about Ancillary Data Help Treat Missing Responses in Ancillary Data?

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.

Media Viewership in the Connected World: A Big Data Case Study

Abstract:

U.S. consumers are adding time to their media day and making time to connect with their favorite content, no matter where it exists (Nielsen 2014). But how they’re consuming media is ever-changing thanks to the continued proliferation of technological devices, 24/7 availability of the media content, ease-of-access, and economics. Whether streaming or satellite, over-the-air or over-the-top, understanding how consumers are consuming media is more important than ever, particularly for companies providing these services since advertising is their major source of revenue. For researchers, this consumption ecosystem has given rise to big datasets consisting of millions and millions of viewing records to mine thru in order to discover trends, viewing patterns, and relationships. In this study, we are attempting to do just that. Read more

A Panel Examination of Over-the-Top Audience

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

Who’s on Netflix vs. Hulu vs. Other? A Panel based examination of SVOD users

Abstract:

The media industry is in a state of flux with continued fragmentation of consumer time and attention around media and across various devices and services. One such service that is popular among consumers today is SVOD (Subscription Video On-Demand) which enables on-demand access to both native digital content and TV-produced content. Forty eight percent of US homes have access to at least one SVOD service from providers such as Netflix, Amazon Prime and Hulu, up from 42% a year ago, according to Nielsen’s report. As consumers are shifting from live viewing to SVOD consumption, researchers are interested in understanding the underlying behavioral changes that are differentiating SVOD service providers. For instance, are consumers watch similar programs between Netflix and Hulu? Are there overlaps and/or uniqueness in consumer behaviors across these service providers? Answering these and many other questions is at the heart of this study and analysis. Read more

David vs. Goliath? Is Over-The-Top Challenging Traditional TV? A Case Study

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

Only for the Young at Heart: Co-Viewing on Mobile Devices and Viewing on the Go?

Abstract:

With the relative ease and accessibility of a variety of content available to users of smartphones and tablets, there has been a subtle behavioral change in how people use these devices. The concept of viewing together or having more than one viewer for a mobile device is a phenomenon referred to as “co-viewing” and is a new area that warrants further investigation. Very little information is available on who is likely to engage in co-viewing behaviors, what types of mobile devices are used, what content is likely to be viewed and if those who engage in this activity / behavior are fundamentally different than those who are less likely—what are the behavioral or demographic differences among those who participate in these activities. Thus the focus here is to examine and provide a baseline understanding around the concept of co-viewing with specific focus of content viewing on the “go” or away from home. Read more

Who Is behind That Screen? Solving the Puzzle of Within-Home Computer Sharing among Household Members

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

Is It Too Much to Ask? The Role of Question Difficulty in Survey Response Accuracy for Measures of Online Behavior

Abstract:

While market research capabilities of online panels have never been greater, the challenges facing these panels in many ways are just as great. Over the past few years, online panels that recruit members using nonprobability/opt-in based methods have come under increased scrutiny and criticism over data quality concerns such as respondent identity and increased satisficing. These concerns have drawn attention to the heart of the issue, which is: the accuracy or truthfulness of data provided by opt-in panel respondents. This issue is of utmost importance given the recently established link between opt-in panel sample and poor survey data quality (see Yeager et. al. 2011). Read more